Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
18265 papers
Chronic pain may alter both affect- and value-related behaviors, which represents a potentially treatable aspect of chronic pain experience. Current understanding of how chronic pain influences the function of brain reward systems, however, is limited. Using a monetary incentive delay task and functional magnetic resonance imaging (fMRI), we measured neural correlates of reward anticipation and outcomes in female participants with the chronic pain condition of fibromyalgia (N = 17) and age-matched, pain-free, female controls (N = 15). We hypothesized that patients would demonstrate lower positive arousal, as well as altered reward anticipation and outcome activity within corticostriatal circuits implicated in reward processing. Patients demonstrated lower arousal ratings as compared with controls, but no group differences were observed for valence, positive arousal, or negative arousal ratings. Group fMRI analyses were conducted to determine predetermined region of interest, nucleus accumbens (NAcc) and medial prefrontal cortex (mPFC), responses to potential gains, potential losses, reward outcomes, and punishment outcomes. Compared with controls, patients demonstrated similar, although slightly reduced, NAcc activity during gain anticipation. Conversely, patients demonstrated dramatically reduced mPFC activity during gain anticipation-possibly related to lower estimated reward probabilities. Further, patients demonstrated normal mPFC activity to reward outcomes, but dramatically heightened mPFC activity to no-loss (nonpunishment) outcomes. In parallel to NAcc and mPFC responses, patients demonstrated slightly reduced activity during reward anticipation in other brain regions, which included the ventral tegmental area, anterior cingulate cortex, and anterior insular cortex. Together, these results implicate altered corticostriatal processing of monetary rewards in chronic pain.
This era, in which we currently stand, is an era of public opinion and mass information. People from all around the globe are joined together through various information junctions to create a global community, where one thing from the far east reaches to the people of the far west within seconds. Nothing is hidden, everything and anything can be scrutinized to its core and through these global criticisms and mass discussions of gigantic magnitude, we have reached to the pinnacle of correct decisions and better choices. These pseudo social groups and data junctions have bombarded our society so much that they now hold the forelock of our opinions and sentiments, ergo, we reach out to these groups to achieve a better outcome. But, all this enormous data and all these opinions cannot be researched by a single person, hence, comes the need of sentiment analysis. In this paper we’ll try to accomplish this by creating a system that will enable us to fetch tweets from twitter and use those tweets against a lexical database which will create a training set and then compare it with the pre-fetched tweets. Through this we will be able to assign a polarity to all the tweets by means of which we can address them as negative, positive or neutral and this is the very foundation of sentiment analysis, so subtle yet so magnificent.
OBJECTIVE: To evaluate variables of tobacco health warnings associated with their emotional impact, the perception of smoking risks and the perceived effectiveness to avoid tobacco use. MATERIALS AND METHODS: Teenagers (151) and adults (168) evaluated 27 tobacco health warnings selected from the sets used on tobacco packages in Argentina and in other countries. A standardized affective rating-scale system and a structured questionnaire measured respectively the emotional impact (hedonic valence and emotional arousal), and the cognitive-behavioral attributions. The correlation between emotional and cognitive-behavioral evaluations was analyzed by age, sex, education level, smoker status,stage of quitting and susceptibility of non-smokers teenagers. RESULTS: Strong significant correlations between cognitivebehavioral and emotional assessments were observed. The warnings depicting graphic images of tobacco-related injuries and suffering were considered more valuable for tobacco. control, helping quitting and preventing initiation. CONCLUSIONS: Using graphic images with high emotional arousal is recommended for both adults and teenagers.
Recurrent Neural Networks (RNNs) play a major role in the field of sequential\nlearning, and have outperformed traditional algorithms on many benchmarks.\nTraining deep RNNs still remains a challenge, and most of the state-of-the-art\nmodels are structured with a transition depth of 2-4 layers. Recurrent Highway\nNetworks (RHNs) were introduced in order to tackle this issue. These have\nachieved state-of-the-art performance on a few benchmarks using a depth of 10\nlayers. However, the performance of this architecture suffers from a\nbottleneck, and ceases to improve when an attempt is made to add more layers.\nIn this work, we analyze the causes for this, and postulate that the main\nsource is the way that the information flows through time. We introduce a novel\nand simple variation for the RHN cell, called Highway State Gating (HSG), which\nallows adding more layers, while continuing to improve performance. By using a\ngating mechanism for the state, we allow the net to "choose" whether to pass\ninformation directly through time, or to gate it. This mechanism also allows\nthe gradient to back-propagate directly through time and, therefore, results in\na slightly faster convergence. We use the Penn Treebank (PTB) dataset as a\nplatform for empirical proof of concept. Empirical results show that the\nimprovement due to Highway State Gating is for all depths, and as the depth\nincreases, the improvement also increases.\n
Despite the advances in information processing systems, word-sense disambiguation tasks are far to be satisfactory as testified by numerous limitations of current translation systems and text inference systems. This paper attempts to investigate new techniques in knowledge based word-sense disambiguation field. First, by exploring the WordNet lexical database and part-of-speech conversion through the established CatVar database that translates all non-noun words into their noun counterparts, and following the spirit of Lesk's disambiguation algorithm, a new disambiguation algorithm that maximizes the overall semantic similarity in the sense of Wu and Palmer measure between each sense of the target word and synsets of words of the context, is established. Second, motivated by the existence of WordNet domains for individual synsets, an overlapping based approach that quantifies the set intersection of synset domains, if not empty, or the hierarchy structure of the domains links through a simple path-length measure is put forward. Third, instead of exploring the whole set of words involved in the context, a selective approach that uses syntactic feature as outputted by Stanford Parser and a fixed length windowing is developed. The developed algorithms are evaluated according to two commonly employed dataset where a clear improvement to the baseline algorithm has been acknowledged.
To approximately parse an unfamiliar language, it helps to have a treebank of a similar language. But what if the closest available treebank still has the wrong word order? We show how to (stochastically) permute the constituents of an existing dependency treebank so that its surface part-of-speech statistics approximately match those of the target language. The parameters of the permutation model can be evaluated for quality by dynamic programming and tuned by gradient descent (up to a local optimum). This optimization procedure yields trees for a new artificial language that resembles the target language. We show that delexicalized parsers for the target language can be successfully trained using such "made to order" artificial languages.
We explore dynamic evaluation, where sequence models are adapted to the recent sequence history using gradient descent, assigning higher probabilities to re-occurring sequential patterns. We develop a dynamic evaluation approach that outperforms existing adaptation approaches in our comparisons. We apply dynamic evaluation to outperform all previous word-level perplexities on the Penn Treebank and WikiText-2 datasets (achieving 51.1 and 44.3 respectively) and all previous character-level cross-entropies on the text8 and Hutter Prize datasets (achieving 1.19 bits/char and 1.08 bits/char respectively).
This study aims to electronically assessed (e-assessment) students’ replies in response to teachers’ question. It can be useful to systematize the question answering context regarding matching text semantically through WordNet semantic similarity techniques. WordNet is a lexical database of words’ synonyms. It uses group of synonyms called synsets for semantical operation of English text. For this purpose, a new methodology is proposed to automate e-assessment in the field of education. The collected dataset contains 210 pairs of words extracted from different undergraduate students’ replies in contradiction of teacher’s question statement. Further WordNet similarity measures i.e. Path Length, Lin, Wu &Palmer and Hirst & Onge are used to compute the semantic relatedness score. In the pilot study 42 pair of words were extracted from 8 students’ replies, which are marked using semantic similarity measures and equated with teacher’s marks. Teachers are provided with four boxes of the mark while our developed method provides a precise measure of marks. The experiment is shown with comprehensive dataset resulting with words’ frequencies in similarity measures.
Rapid advances in information technology and proliferation of social media services have caused a radical transformation of human communication. Having created a social media presence people engage in computer-mediated communication, set their own goals as well as perfect their knowledge of English as a global language. The richness and diversity of computer-mediated discourse is concentrated in multiple online experiences and therefore enables to study a great number of linguistic changes. Various studies of computer-mediated discourse analyze socio-psychological characteristics in coherent sequences of sentences, propositions, speech or turns-at-talk. The given article aims at presenting vocabulary teaching strategies to new computer-mediated language and their influence on students' acquisition. Our contribution provides an overview of the recent new entries of computer-mediated vocabulary in online crowdsourced dictionaries. The main ways of forming new words as well as wide-spread semantic changes are viewed (acronyms, compounds, suffixes, blended words, conversion, etc.) Computer-mediated vocabulary teaching in the classroom covers change, diversity, disputes in economical, political, social spheres (such as narcissistic tendencies, emotional correctness, excessive use of social media and increasing reliance on technology, equal rights movement, task-based employment, etc). As the social media universe strives for a thorough integration with the user's life language learners are expected to embrace the latest changes in linguistic norms and devise an appropriate philosophy of language management.
This paper describes our system (SLT-Interactions) for the CoNLL 2018 shared task: Multilingual Parsing from Raw Text to Universal Dependencies. Our system performs three main tasks: word segmentation (only for few treebanks), POS tagging and parsing. While segmentation is learned separately, we use neural stacking for joint learning of POS tagging and parsing tasks. For all the tasks, we employ simple neural network architectures that rely on long short-term memory (LSTM) networks for learning task-dependent features. At the basis of our parser, we use an arc-standard algorithm with Swap action for general non-projective parsing. Additionally, we use neural stacking as a knowledge transfer mechanism for cross-domain parsing of low resource domains. Our system shows substantial gains against the UDPipe baseline, with an average improvement of 4.18% in LAS across all languages. Overall, we are placed at the 12 th position on the official test sets.
Tree-structured neural network architectures for sentence encoding draw inspiration from the approach to semantic composition generally seen in formal linguistics, and have shown empirical improvements over comparable sequence models by doing so. Moreover, adding multiplicative interaction terms to the composition functions in these models can yield significant further improvements. However, existing compositional approaches that adopt such a powerful composition function scale poorly, with parameter counts exploding as model dimension or vocabulary size grows. We introduce the Lifted Matrix-Space model, which uses a global transformation to map vector word embeddings to matrices, which can then be composed via an operation based on matrix-matrix multiplication. Its composition function effectively transmits a larger number of activations across layers with relatively few model parameters. We evaluate our model on the Stanford NLI corpus, the Multi-Genre NLI corpus, and the Stanford Sentiment Treebank and find that it consistently outperforms TreeLSTM
In this paper we present the linguistic databases developed during our 8-year lexicographic research on the Modern Greek Standard (MGS) verbal system. Apart from the intermediate databases presented, the main products are (a) a new conjugation system of 385 paradigmatic models, which allows for the automatic generation of all verbal lexical morphemes and monolexical forms (b) a statistically established database of 151,536 distinctive verb-final grapheme sequences which allow for the automatic tagging of all monolexical verbal tokens without the traditional intervention of any built-in lexicon, and (c) a linear Iemmatisation morphophonological rule system accessed on the basis of the distinctive grapheme sequences identified.
Sarcasm is a sophisticated form of sentiment expression where speaker express their opinions opposite of what they mean. Sarcasm detection and Emotion detection from social net-working sites has been a great field of study. With the growth of e-services such as e-commerce, e-tourism and e-business, the companies are very keen on exploiting emotion and sarcasm analysis for their marketing strategies in order to evaluate the public attitudes towards their brand. Thus efficient emotion and sarcasm modeling system can be a good solution to the above problem. This work aims at developing a system that groups posts based on emotions, sentiment and find sarcastic posts, if present. The proposed system is to develop a prototype that help to come to an inference about the emotions of the posts namely anger, surprise, happy, fear, sorrow, trust, anticipation and disgust with three sentic levels in each. This helps in better understanding of the posts when compared to the approaches which senses the polarity of the posts and gives just their sentiments i.e., positive, negative or neutral. The posts handling these emotions might be sarcastic too. The Sentiment & emotion identification module identifies the sentiment or emotion of the post by evaluating score of each word in the comment which is used by different sarcasm detection methods to detect sarcasm. The emotion identification module uses the lexical databases WordNet, SentiWordNet to find the right sentiment scores for the words with respect to each emotion. It also uses Sarcasm detection algorithms like Emoticon sarcasm detection, Hybrid sarcasm detection, Hashtag Processing, Interjection Word Start (IWT).
Detecting lexical entailment plays a fundamental role in a variety of natural language processing tasks and is key to language understanding. Unsupervised methods still play an important role due to the lack of coverage of lexical databases in some domains and languages. Most of the previous approaches were either based on statistical hypothesis of specific entailment relations or tried to encode word relations in low-dimensional vector embeddings. This thesis builds upon one of the few approaches which intrinsically model entailment in a vector space. We then further generalize this model by introducing an alternative, distributional representations for words which harnesses tools from optimal transport to define distance or entailment measures between such representations. We evaluated the models on hypernymy detection where our distributional estimate significantly improves over the underlying model and even outperforms state-of-the-art on some datasets.
The statistical parsing of morphologically rich languages is hindered by the inability of parsers to collect solid statistics because of the large number of word types in such languages. There are however two separate but connected problems, reducing data sparsity of known words and handling rare and unknown words. Methods for tackling one problem may inadvertently negatively impact methods to handle the other. We perform a tightly controlled set of experiments to reduce data sparsity through class-based representations in combination with unknown word signatures with two PCFG-LA parsers that handle rare and unknown words differently on the German TiGer treebank. We demonstrate that methods that have improved results for other languages do not transfer directly to German, and that we can obtain better results using a simplistic model rather than a more generalized model for rare and unknown word handling.
Abstract People remember events and materials better when these are congruent with their mood at retrieval; this is known as the mood-congruent memory bias. This effect is largest when the materials are self-referential and this is known as the self-reference effect. We present two word rating studies, to create a list of self-referential valenced words that may be used as stimuli to investigate the influence of valence on cognitive processing in depressive ruminators. Words selected from the Affective Norms for English Words pool were rated by an unselected sample for self-referentiality (Study 1) and validated with ratings provided by depressive ruminators. As hypothesized, depressive ruminators rated negative words as more self-referential than an unselected sample. Using this list, valence differentiated performance between depressive ruminators and healthy controls in a working memory updating task. We thus created a list of self-referential valenced words matched on factors that influence word processing.
This paper aims to observe, describe and explain the translation of film titles with the Chinese character "Xia". In this paper, a small parallel corpus is firstly constructed and annotated to analyze the basic information, language features, translation principles, strategies, methods and techniques of film titles. And then, based on Holmes' methodology of Descriptive Translation Studies, some descriptive corpus is analyzed and some assumptions are predicated. Finally, these assumptions are divided into three preliminary norms, two initial norms, two matrix norms and three textual-linguistic norms under the guidance of Toury's translation norm system with a view to provide some references and inspiration for the standardization of film title translation.
This study aims at exploring new norms as to the textual additions in parentheses (=TAiPs) in the translation of a Quranic text as writer-oriented devices of textuality. Coding for this sort of information could be useful in establishing an impact on any decision-making process on the TL version; such TAiPs can give a translated text of the Quran unity and purpose and distinguish it from a disconnected sequence of sentences. Six small-sized chapters of the Quran were selected as a research sample including a number of four handred forty two (442) TAiPs. Two writer-oriented kinds of textuality were found: cohesivity at the levels of grammar and lexis to be in form of recurrence, reference, substitution, ellipsis and conjunction; and relationality by coherence and intentionality to be in form of reiteration, collocation, connotation, evocation and interpretation. The study is a detailed analysis of such a severely criticized yet officially approved English interpretation of the Quran as the Hilali and Khan Translation (=HKT) against a predetermined set of text-linguistic norms. The strength or weakness of TAiPs as to how they might alleviate or aggravate the TL version is eventually identified for sake of improvement.
We propose the dense RNN, which has the fully connections from each hidden state to multiple preceding hidden states of all layers directly. As the density of the connection increases, the number of paths through which the gradient flows can be increased. It increases the magnitude of gradients, which help to prevent the vanishing gradient problem in time. Larger gradients, however, can also cause exploding gradient problem. To complement the trade-off between two problems, we propose an attention gate, which controls the amounts of gradient flows. We describe the relation between the attention gate and the gradient flows by approximation. The experiment on the language modeling using Penn Treebank corpus shows dense connections with the attention gate improve the model’s performance.
The article is devoted to the problem of identifying the stylistic functions of addresses, which are used in Internet communication. To achieve this goal, the author has solved several problems. For the first, the main features of Internet communication are anonymity, mediation, distance and frequent violation of linguistic norms. The last attribute refers to the adresses, which are used in Internet messages. For the second, official and household addreses are used in the Internet communication,. The stylistic function of the official addresses is the indication of the status and social role of the addressee. The stylistic function of household appeals is the indication of proximity between communication participants, the emphasis on positive or negative connotations of addresses.In addition, household addresses reflect the new social trends associated with the using of e-mail, aliases etc. The main conclusions of this research: the greatest number of the addresses in Internet communication is recorded in the materials of business correspondence. The users of the network very rarely use the household addresses. The author believes that the main reason for such quantitative dynamics is the avoidance or inability of addressees to show an emotional attitude to their interlocutor.
We investigated user experiences from 117 Finnish children aged between 8 and 12 years in a trial of an English language learning programme that used automatic speech recognition (ASR). We used measures that encompassed both affective reactions and questions tapping into the children' sense of pedagogical utility. We also tested their perception of sound quality and compared reactions of game and nongame-based versions of the application. Results showed that children expressed higher affective ratings for the game compared to nongame version of the application. Children also expressed a preference to play with a friend compared to playing alone or playing within a group. They found that assessment of their speech is useful although they did not necessarily enjoy hearing their own voices. The results are discussed in terms of the implications for user interface (UI) design in speech learning applications for children.
In Russom (2011), I defended a universalist hypothesis that the constituents of poetic form are abstracted from natural linguistic constituents: metrical positions from phonological constituents, usually syllables; metrical feet from morphological constituents, usually words; and metrical lines from syntactic constituents, usually sentences. An important corollary to this hypothesis is that norms for realization of a metrical constituent are based on norms for the corresponding linguistic constituent. Optimality Theory provides a universalist account of relevant linguistic norms and deals effectively with situations in which norms conflict, employing ranked violable rules. Language Typology provides a universalist account of relevant syntactic norms. In this paper I integrate these independently grounded methodologies and use them to explain the distribution of constituents within the line, identifying a variety of important facts that seem to have escaped previous notice. Universalist claims are tested against meters from each of the major language types: subject-verb-object (SVO), subject-object-verb (SOV) and verb-subject-object (VSO). My findings are incompatible with the claim that “lines are sequences of syllables, rather than of words or phrases” (Fabb, Halle 2008: 11).
In this paper, we describe our project of building a phrase structure treebank for Persian. The treebank consists of approximately 30000 sentences. With the help of this treebank, the researcher can investigate syntactic phenomena, extract grammars, train and test parsers, etc. In addition to these motivations, as another advantage of it we can refer to the fact that the sentences of this treebank are selected from an available dependency treebank. So the final treebank has two syntactic representations: phrase structure and dependency structure. The treebank is built using a bootstrapping approach, which converts a dependency structure tree to a phrase structure tree and the annotations are corrected manually. Using the new phrase structure treebank, we train models for constituency parsers. The treebank is freely available for educational purposes <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.
Democratization in speech have not only broadened the ways of language expression, manifestations of linguistic individuality, but have led to many negative phenomena. This is typical not only for marginal communication, but also for political discourse, especially � for the media, which has a huge impact on the speech behavior of society. Nowadays, the concept of ethical and linguistic standard have been actualized, it is developed not only in the framework of ecological linguistics, but also in legal linguistics. In the context of ethical and speech norms, it is important to note the words usage is inseparable from the categories of ethics. These new phenomena are due to the combination of all the circumstances of socio-political and cultural life. It is impossible to give any recommendations in the field of regulation in general and ethical and linguistic norms in particular without taking them into account. The methodology of the work is based on a combination of panchronic and diachronic approaches to the language. The leading method is extrapolation of language theories, which arose in the same historical conditions, to the conditions of different historical reality, synthesis of interpretative and comparative approaches to the material, component-semantic and contextual analysis, composite analysis.
Based on Chinese dependency treebank PMT 1.0, the present study investigates the positional aspects of dependency distance (DD) quantitatively. Results show that (1) as the word position in the sentence increases, the tendency of mean dependency distance (MDD) in different sentence length groups shows striking similarity. The two longest MDDs generally are in the sentence-initial and sentence-final positions; (2) The consensus string (CS) and weighted consensus string (WCS) show some characteristics, which demonstrates again that human cognition plays an important role in affecting DD and dependency distance minimization is a universal tendency; (3) The distribution of DD in each sentential position can be captured by power law, which implies that something like a vertical structure of texts exists.
I participated in the CMLD (Computational Methods for Endangered Languages) conference last week at ENS in Paris. Laurent Besacier was also attending. Here is a short report.-About fifty participants, several were also present at the workshop on Uralic languages in Saint Petersburg a year ago-Proposal to use facial recognition for metadata on speakers' names (Niko Partanen, one of the organizers).-Joachim Nivre presented the international effort on "Universal Dependencies (Treebanks)". Already 60 languages. I ask him what he plans to do for the remaining 6840... He says it's a funding problem. He agrees that they should be treated by family of languages to save money.-Presentation by Jargal Badagarov (Mongolia:
We describe the first automatic approach for merging coreference annotations obtained from multiple annotators into a single gold standard. This merging is subject to certain linguistic hard constraints and optimization criteria that prefer solutions with minimal divergence from annotators. The representation involves an equivalence relation over a large number of elements. We use Answer Set Programming to describe two representations of the problem and four objective functions suitable for different datasets. We provide two structurally different real-world benchmark datasets based on the METU-Sabanci Turkish Treebank and we report our experiences in using the Gringo, Clasp, and Wasp tools for computing optimal adjudication results on these datasets.
The Norm in Translation and the Feeling of Lack – an Attempt of Psychoanalytical Reflection on the Experience of Translation This article is an attempt to use psychoanalytical language for describing the experience of translation and the discourse on translation. While considering the recognitions and method proposed by Tadeusz Sławek in an article titled, „Kalibanizm. Filozoficzne dylematy tłumaczenia“ [Kalibanism. The philosophic dilemma of translation], I try to describe them and develop, to say a few words about the translation and linguistic norm in the context of feeling of lack widely spread in the discourse on translation. Thinking about translation process as one affected by the melancholy I try to point out the indelible contradiction between the existence of translation norm and the imaginative concept of semantic plenitude of the original text. Repeating after Sławek I make an attempt to indicate a “crypt” that is being build by the target language around the original work, a crpyt existence of which was omitted with silence, although it have had a grate influence on the way that translation funcion in our culture.
Based on research linking depressive symptoms and intimate partner aggression perpetration with negatively biased perception of social stimuli, the present authors examined biased perception of emotional expressions as a mechanism in the frequently observed relationship between depression and psychological aggression perpetration.In all, 30 university students made valence ratings (negative to positive) of emotional facial expressions and completed measures of depressive symptoms and psychological aggression perpetration.As expected, depressive symptoms were positively associated with psychological aggression perpetration in an individual's current relationship, and this relationship was mediated by ratings of negative emotional expressions.These findings suggest that negatively biased perception of emotional expressions within the context of elevated depressive symptoms may represent an early stage of information processing that leads to aggressive relationship behaviors.
STUDY DESIGN: Descriptive analysis using publicly available data. OBJECTIVES: The purpose of this study was 2-fold: to assess patient-rated trustworthiness of spine surgeons as a whole and to assess if academic proclivity, region of practice, or physician sex affects ratings of patient perceived trust. METHODS: Orthopedic spine surgeons were randomly selected from the North American Spine Society directory. Surgeon profiles on 3 online physician rating websites, HealthGrades, Vitals, and RateMDs were analyzed for patient-reported trustworthiness. Whether or not the surgeon had published a PubMed-indexed paper in 2016 was assessed with regard to trustworthiness scores. Total number of publications was also assessed. Individuals with >300 publications were excluded due to the likelihood of repeat names. RESULTS: Recent publication and total number of publications has no relationship with online patient ratings of trustworthiness across all surgeons in this study. Region of practice likewise has no influence on mean trust ratings, yet varied levels of correlation are observed. Furthermore, there was no difference in trust scores between male and female surgeons. CONCLUSION: Total academic proclivity via indexed publications does not correlate with patient perceived physician trustworthiness among spine surgeons as reported on physician review websites. Furthermore, region of practice within the United States does not have an influence on these trust scores. Likewise, there is no difference in trust score between female and male spine surgeons. This study also highlights an increasing utility for physician rating websites in spine surgery for evaluating and monitoring patient perception.
Images and language convey meaning that depend on the viewpoints and contextual background of those perceiving them. Taxonomies help order meaning such that information granules, image elements and words, make sense in relation to one another and to their mutual global context. In linguistics, hypernyms cover semantically broader context then their subordinate hyponym. In images, superordinate spatial-taxons (object groups or foreground) cover more abstract regions then their child subordinate spatial-taxons (objects or salient object parts). In this paper I use fuzzy granularization and fuzzy perceptualization as proposed by Zadeh 2002 to explore image annotation by using Zadeh's Restriction-centered Theory of Truth and Meaning as proposed in 2013. The approach uses human annotated image data, search engine queries and data collected from WordNet (A Lexical Database for English maintained by Princeton University). I discuss implications for Shannon, Integrated, and Zadeh Information Theory.
The article presents a quantitative analysis of some syntactic dependency properties in Czech. A dependency frame is introduced as a linguistic unit and its characteristics are investigated. In particular, a ranked frequencies of dependency frames are observed and modelled and a relationship between particular syntactic functions and the number of dependency frames is examined. For the analysis, the Czech Universal Dependency Treebank is used.
Let me begin by thanking the Association for Computational Linguistics and its Executive Committee for conferring on me the great honor of their Lifetime Achievement Award for 2018, which of course I share with all the wonderful students and colleagues that have made many essential contributions to this work over many years.At the heart of the work that I have been pursuing over my research lifetime so far, whether in parsing and sentence processing, spoken language understanding, semantics, or even in musical understanding by machine, there lies a theory of natural language grammar that brings parsing, compositional semantics, statistical modeling, and logical inference into the closest possible relation. This theory of grammar is combinatory, in the sense that its operations are type-dependent and restricted to strictly string-adjacent phonologically or graphologically-realized inputs, and categorial, in the sense that those operands pair a syntactic type with a type-transparent semantic representation or logical form.I'd like to use this opportunity to briefly address three questions that revolve around the theory of grammar, both combinatory and otherwise. The first question concerns the way that Combinatory Categorial Grammar (CCG) was developed with a number of colleagues, over a number of stages and in slightly different forms. The second is an essentially evolutionary question of why natural language grammar should take a combinatory form. The third question is that of what the future holds for CCG and other structural theories of grammar in computational linguistics and NLP in the age of deep learning.I have called this talk "The Lost Combinator" in homage to the Victorian era poem "The Lost Chord," in the hope of suggesting that the theoretical development of CCG has always been empirical, rather than axiomatic, in search of the simplest explanation of the facts of language, rather than for confirmation of linguistic received opinion, however intuitively salient.In the late 1960s (when I was a psychology undergraduate at the University of Sussex under Stuart Sutherland, and then started as a graduate student in artificial intelligence at Edinburgh under Christopher Longuet-Higgins), a broad community of theoretical linguists, psychologists, and computational linguists saw themselves as all working on the same problem, under the definition provided by the "transformational" theory of grammar proposed by Chomsky (1957, 1965), using theories of psycholinguistic processing, language acquisition, and language evolution proposed by Lashley (1951), Miller, Galanter, and Pribram (1960), Miller (1967), and Lenneberg (1967), theories of natural language semantics proposed by Carnap (1956), Montague (1970), and Lewis (1970), and computational models of parsing such as those proposed by Thorne, Bratley, and Dewar (1968) and Woods (1970). (I myself was so convinced that this program would succeed that I believed it was time to apply the same methods to other cognitive faculties, taking as my research project for Ph.D. their application to the interpretation of music by machine, following the lead of Max Clowes [1971] in machine vision.)Almost immediately, this consensus fell apart. First, Chomsky himself was among the first (1965) to recognize that transformational rules, though descriptively revealing, were so expressive as to have little explanatory force, and required many apparently arbitrary constraints (Ross 1967). Second, psychologists realized that psycholinguistic measures of processing difficulty of sentences bore almost no relation to their transformational derivational complexity (Marslen-Wilson 1973; Fodor, Bever, and Garrett 1974). Finally, computational linguists attempting to implement transformational grammars as parsers realized that they were spending all their time implementing even more constraints on rules, in order to limit search arising from overgeneration (Friedman 1971; Gross 1978). (Meanwhile, I realized that the problem had not in fact been solved, and returned to natural language processing, thanks to a postdoc at Sussex with Philip Johnson-Laird.)This disillusion wasn't just a case of internal academic squabbling. There were also a couple of influential reports commissioned by the U.S. and UK governments that ended funding for machine translation (MT) and artificial intelligence (AI) (Pierce et al. 1966; Lighthill 1973). As a result of the second of these reports, which determined that AI was never going to work, PhDs in artificial intelligence like my classmate Geoff Hinton and myself spent ten years or so after graduation in psychology departments (in my case, at the Universities of Sussex and Warwick), until yet another report said AI was working after all and that Britain and the U.S. were falling behind Japan in this vital area. As a result, I could get hired again in computer science, first briefly back at Edinburgh, and then at the University of Pennsylvania (I learned a lesson from this odyssey that I have tried to remember whenever I have been appointed to a committee to report on anything, which is that while reports very rarely do any good, they can very easily do a great deal of harm.)Meanwhile, as a result of these conflicts, the scientific study of language fragmented. The linguists swiftly abjured any responsibility for their grammars ("Competence") bearing any relation to processing ("Performance"). Because the psychologists could hardly abandon Performance, they in turn became agnostic about grammar, retreating to context-free surface grammar (which they tended to refer to as "parsing strategies"), or a touchingly optimistic belief in its emergence from neural models. Meanwhile, the computational linguists (whose machines were growing exponentially in size and speed from the 16K byte core of the machine that supported the whole group when I started my graduate studies, on to levels that would soon permit parsing the entire contents of the then embrionic Web) similarly found that very little of what the linguists and psychologists cared about was usable at scale, and that none of it significantly improved overall performance over very much simpler context-free or even finite-state methods that the linguists had shown to be incomplete. The reason of course was Zipf's law, which means that the events with respect to which the low-level methods are incomplete are off in the long tail.It also became apparent to a few computationalists working on speech, MT, and information retrieval that the real problem was not grammar but ambiguity and its resolution by world-knowledge, and that the solution lay in probabilistic models (Bar-Hillel 1960/1964; Spärck Jones 1964/1986; Wilks 1975; Jelinek and Lafferty 1991) (although it was not immediately apparent how to combine statistical models with grammar-based systems without making obviously false independence assumptions).Nevertheless, as any red-blooded psychologist had always insisted, the divorce between competence and performance that everyone else had accepted did not make any sense. The grammar and the processor had to have evolved in lock-step, as a package deal, for what could be the evolutionary selective advantage of a grammar that you cannot process, or a parser without a grammar?It seemed equally obvious that surface syntax and the underlying semantic or conceptual representation must also be closely related, since the only reasonable basis for child language acquisition that has ever been on offer is that the child attaches language-specific grammar to a universal conceptual relation or "language of mind" (Miller 1967; Bowerman 1973; Wexler and Culicover 1980). It seemed to follow that radically new theories of grammar were needed.Theoretical linguists agree that the central problem for the theory of grammar is discontinuity or non-adjacent dependency between predicates and their arguments:Chomsky described discontinuity in terms of movement, which was known to be formally very unconstrained. By contrast, the ATN parser used in the LUNAR project (Woods, Kaplan, and Nash-Webber 1972) reduced all discontinuity to local operations on registers (Thorne, Bratley, and Dewar 1968; Bobrow and Fraser 1969; Woods 1970).In particular, unbounded wh-dependencies like the above were handled by: (a) putting a pointer into a * or HOLD register as soon as the "which" was encountered without regard to where it would end up; and (b) retrieving the pointer from HOLD when the verb needing an object "had" was encountered without regard to where it had started out. (It also included an ingenious mechanism for coordination called SYSCONJ, which one finds even now being reinvented on an almost yearly basis—cf. Woods [2010].) A * register was also used for wh-constructions within a systemic grammar framework by Winograd (1972, pages 52–53) in his inspiring conversational program SHRDLU.However, it was unclear how to generalize the HOLD register to handle the multiple long-range dependencies, including crossing dependencies, that are found in many other languages. In particular, if the HOLD register were assumed to be a stack, then the ATN becomes a two-stack machine (since we are already implicitly using one stack as a PDA to parse the context-free core grammar).On the computational side at least, the reaction to this impass took two distinct forms. Both reactions took the form of trying to reduce the two major operators of the transformation theory, substitution of immediate constituents, or what is nowadays called "Merge," and "Move," or displacement of non-immediate constituents, to one. On the one hand, Lexical Functional Grammar (Bresnan and Kaplan 1982) and Head-driven Phrase Structure Grammar (Pollard and Sag 1994) followed Kay (1979) in making unification the basis of movement and merger. Because unification can pass information across unbounded structures, this can be thought of as reducing Merge to Move.On the other hand, Generalized Phrase Structure Grammar (Gazdar 1981), Tree Adjoining Grammar (TAG; Joshi and Levy 1982), and Combinatory Categorial Grammar (CCG, Ades and Steedman, 1982) sought to reduce Move to various forms of local merger. In particular, the latter authors suggested that the same stack could be used to capture both long-range dependency and recursion in CCG.1Natural language grammar exhibits discontinuity because semantically language is an applicative system. Applicative systems (such as programming languages) support the twin notions of: (a) Application of a function/concept to an argument/entity; and (b) Abstraction, or the definition of a new function/concept in terms of existing ones.Language is in that sense inherently computational. It seems to follow that linguistics is (or should be) inherently computational as well. (Of course, it does not follow that computationalists have nothing to learn from linguistics.)There are two ways of modeling abstraction in applicative systems: Taking abstraction itself as a primitive operation (λ-calculus, LISP):(2)a.fatherEsau⇒Isaacb.grandfather=λx.father(fatherx)c.grandfatherEsau⇒Abrahamor Defining abstraction in terms of a collection of operators on strictly adjacent terms aka Combinators, such as function composition (Combinatory Calculus, MIRANDA).(3)b′.grandfather=BfatherfatherThe latter does the work of the λ-calculus without using any variables.Despite the resemblance of the "traces" (or copies) and "operators" (or complementizer positions) of the transformational theory to the λ-operators and variables of applicative systems of the first kind, natural language actually seems to be a system of the second, combinatory kind. The evidence stems from the fact that natural language deals with all sorts of fragments that linguists do not normally think of as semantically typable constituents, without the use of any phonologically realized equivalent of variables, such as pronouns:(4)a.Give[Anna books]?and[Manny records]?b.(Mother to child): There's adoggie![Youlike]?#the doggie.c.Food that you must[washVP/NP[before eating](VP∖VP)/NP]?.d.ik denk dat ik1Henk2Cecilia3[zag1leren2zingen3]?These fragments are diagnostic of a Combinatory Calculus based on Bn, T, and the "duplicator" Sn, plus application (Steedman 1987; Szabolcsi 1989; Steedman and Baldridge 2011).2CCG lexicalizes all bounded dependencies, such as passive, raising, control, exceptional case-marking, and so forth, via lexical logical form. All syntactic rules are Combinatory—that is, binary operators over contiguous phonologically realized categories and their logical forms. These rules are restricted by a Combinatory Projection Principle, which in essence says they cannot override the decisions already taken in the language-specific lexicon, but must be consistent with and project unchanged the directionality specified there. All such language-specific information is specified in the lexicon: The combinatory rules like composition are free and universal. All arguments, such as subjects and objects, are lexically type-raised to be functions over the predicate, as if they were morphologically cased as in Latin, exchanging the roles of predicate and argument.All long-range dependencies are established by contiguous reduction of a wh-element, such as (N∖N)/(S/NP), with an adjacent non-standard constituent with category S/NP, formed by rules of function composition.The combinatory rules synchronize composition of the syntactic types shown here with corresponding composition of logical forms (suppressed in the derivations above), to yield the logical forms shown as λ-terms for the resulting nouns N.To capture the construction in Example (4c), whose syntactic derivation we pass over here, we also need rules based on the duplicator S:(7)a."wash X before eating X″b.VP/NP:λx.before(eatx)(washx)≡S(Bbeforeeat)washTo capture constructions like Example (4d) (whose syntactic derivation is similarly suppressed), we also need rules based on second-order composition B2:(8)a."Y saw X teach W to sing.″b.((S∖NP)∖NP)∖NP:λwλxλy.help(teach(singw)wx)xy≡B2sees(Bteachsing)CCG thus reduces the operator move of transformational theory to applications of purely adjacent operators—that is, to recursive combinatory merge.Interestingly, the latest "minimalist" form of the transformational theory has also proposed that Move should be relabeled as an "internal" form of standard or "external" Merge (Chomsky 2001/2004, page 110), though without providing any formal basis for the reduction other than identifying internal Merge as "a grammatical transformation." (If anything deserved the soubriquet "the lost combinator," it would be this notional unitary combination of application and abstraction in a single perfect operator, linking or merging all types, as in the epigraph to this article.)B2 rules allow us to "grow" categories of arbitrarily high valency, such as ((S∖NPy)∖NPx)∖NPw. As we saw earlier, in some Germanic languages like Dutch, Swiss German, and West-Flemish, serial verbs are linearized using such rules to require crossing discontinuous dependencies. Thus, B2 rules give CCG slightly greater than context-free power.Nevertheless, CCG is still not as expressive as movement. In particular, we can only capture permutations that are what is called "separable," where separability is related to the idea of obtaining the permutations by rebracketing and rotating sister nodes (Steedman 2018).For example, for the categories of the form A|B, B|C, C|D, and D, it is obvious by inspection that we cannot recognize the following permutations:(9)i.*B|CDA|BC|Dii.*C|DA|BDB|CThis generalizatiion appears likely to be true cross-linguistically for the components of this form for the NP "These five young boys":(10)i.*Five boys these youngii.*Young these boys fiveTwenty-one of the 22 separable permutations of "These five young boys" are attested (Cinque 2005; Nchare 2012). The two forbidden orders are among the unattested three.3The probability of this happening by chance is the probability of the is the of the number of ways of two of three unattested by the is, about one in a (If the order that unattested so were to be this chance would to about one in number of separable permutations much more in than the number of all example, for around of the permutations are There are obvious for the problem of in machine translation and neural semantic parsing, to which we I to and at first as a still under least, that was what Joshi and then as a of where much of the development of CCG was out. students in a of and Joshi 1987; and that the of Ades and Steedman was by that both CCG and were equivalent to Grammar (Gazdar a new of the by the and of languages by these fell within the of what Joshi called which proposed as a for what could as a theory of natural that they are and and some limit on crossing dependencies. the is much much than the including the multiple free languages and even the languages of so it seems to and CCG as with to the its CCG was assumed to be as a grammar for parsing, because of the derivational ambiguity by and the combinatory rules, these also under and as any grammar with the same as CCG the same of in the parser it is there in the In this is just another in the of derivational ambiguity that all natural language and can be handled by the same statistical models as other particular, the dependency models by and are and Steedman and CCG is also to parsing with which can be using and long and Steedman and is now used in those that for between semantic and syntactic processing, such as machine translation and and machine and parsing and Steedman 1987; and et al. et al. et al. and semantic parser and 2005; et al. et al. of my work with the same has returned to their application in musical and and Steedman and shown that CCG grammars of the same and parsing models of the same statistical are required there as It is only in the of their compositional semantics that music and language very than on this of work in like to by two more The first is an evolutionary should natural language be a combinatory in the first The second is a question about the future development of CCG and other grammar-based theories to be to NLP in the age of deep and recursive neural take these questions in order in the two language like a combinatory applicative system because T, and evolved in to support of before there was any language (Steedman like need and are for of need to to make can form the that you need to before is to that they allow and some other can with like B2 are to make with arbitrary of including and including other whose is yet to be to be to do the the and problem, using like to in order to that are of to of and so such as of and can apply to a and much work that the and other have to be there in the already for the to be to with was to that from the even if had the problem of can be as the problem of search for a of in a or of possible such search has the same recursive as parser example, there are both and for the The latter a more in evolutionary as a mechanism that is to both semantic interpretation and parsing, rather than the evolution of like the the for linguistic as as the operators for competence grammar, the two to as what was to above as an evolutionary hope to have convinced you that CCG grammars are both and as as semantic parsers that are to parse the like other CCG parsers are by the of parsing models based on only a of no how we using and the constructions they are dependencies and so as we have and off in the long As a parsers are to on performance overall by using models et al. says more about the of grammars and parsers than about the of deep in the of semantic parser for arbitrary such as and Steedman I would that CCG and other grammar-based parsers have already been by of deep neural and and the question of whether models for applications in are because we have to the universal semantic that allow the child to CCG for natural languages and that we to be using both in semantic parser and in Because the language is any of linguistic logical like the universal language of it be more with and to semantic parsers for by deep neural force, rather than by CCG semantic parser is it possible that the problem of parsing could be by neural by stack et al. et al. actually learn as has been seems likely that semantic parsers and neural machine translation to have difficulty with long-range because the evidence for their is so example, both and as a case, verb categories or a complementizer I is actually by which says that if you are an language with like and then you not in be to in languages like or languages like and German, you be to both subjects and objects, or at the time of a translation system no of learned these syntactic from a we get an sentence whose to translation means is the that the us the a is the that the us to the if we with we (which back into the a is the that the us holds the a we with using a the translation again an that is is the that they said had the is the they said they the contrast, CCG parsers do rather on and Steedman Steedman, and which is a construction that to be rather determined by the parsing methods are similarly when with long-range even when the sentence is in this think that the the and that it is et think that the the and it is in at least, these constructions could be learned by grammar-based semantic parser from using the methods of et al. and et al. make it likely that there be a need for in like where long-range dependencies like deep and are here to The future in parsing for such lies with systems using neural for and grammars for problem in NLP the fact that natural language understanding inference as as semantics, and we have no idea of the representation question is the almost it many it is almost equally to the information in a form that is not immediately with the form of the example, sentences like the following a different a rather than a an an a a and at a representation language that is we are using CCG parsers to the for between in order to consistent of between over of the same types, using over then an it and it under such as et al. of in the then that can be to a single relation and Steedman can be across from multiple languages and Steedman can then the semantics for relation with the and the entire using this now both and semantic an with the as and the as questions the in this we parse questions into the same semantics which is now the language of the the we use the and the and the following anything that in the then the the of anything is in the in the this to work, we need to be nodes in their in both and then be to like in of a semantic in the language of the need to learn between semantic and the language of the this project is the the function of a of the semantic in semantic like those of and and Steedman and while the form a similarly of the of Carnap and Fodor, Fodor, and Garrett semantic are essentially but with the advantage that they can be with logical operators such as and for the of semantic underlying natural language semantics in to another different to semantics that to use reduced of to using operations such as and and in of compositional It is an question whether can be with to of a and et al. It is likely that some of be here to the of the of the long like and work in do they work in In particular, can they learn all the syntactic in the long like and crossing in a way that support semantic they are not actually but are a finite-state or a then by on as for natural language processing, we are in of of the computational linguistic project of also providing computational of language and if we that like is a real and that learn their first language by of the sentences of their language the of the universal language of we still the of what that universal semantic language not get an to that question we can above using and and such as as for the language of to use machine for what it is such variables and their for use in a natural language work was supported in by a Award and a a University of Edinburgh and my and the and all my and students over many
The effect of emotion on memory is powerful and complex. While there seems to be agreement that emotional arousal generally increases the likelihood that events are remembered, it is somewhat disputed whether also the valence of emotions influences memory. Specifically, several experiments by Kensinger and colleagues have provided evidence for the hypotheses that negative valanced emotions facilitate the encoding of perceptual details. On the other hand, Mather and colleagues have suggested that these results could be explained by confounding relationships of valence and arousal, i.e., that items that generate negative emotions are typically also more arousing. In this study, we provide a conceptual replication of Kensinger's findings. We employed a novel experimental design, in which the effects of standardized emotional arousing sounds on recognition accuracy for neutral visual scenes was measured. We indirectly manipulated the amount of visual detail that was encoded, by requiring participants to memorize either single exemplars (low interference) or multiple exemplars (high interference) of visual scene categories. With increasing visual overlap in the high interference condition, participants were required to encode a high degree of visual detail to successfully remember the exemplars. The results obtained from 60 healthy human participants confirmed Kensinger's hypothesis by showing that under conditions of high visual interference, negative valanced emotions led to higher levels of recognition accuracy compared to neutral and positive emotions. Furthermore, based on the normative arousal ratings of the stimulus set, our results suggest that the differential recognition effect cannot be explained by differing levels of arousal.
The peculiar phenomenon in the field of mass communication is religious periodicals. On the one hand, it has common peculiarities by which periodicals are characterized, on the other hand it has a special communicative purpose and a range of topics covered. The aim of the article is to carry out a general analysis of the lexical composition of religious Christian periodical texts. The classification of the periodical religious publication lexical composition was carried out in the article on the publication of the newspaper «Volyn diocesan reports» (2004–2018). The specificity of the lexical composition is determined by topics of publications. In religious periodicals, first of all, problems of faith, spirituality, Christian ethics, norms of moral behavior of a Christian, church and religious life are violated, as a result of which the vocabulary on named realities designation becomes a stylish one. Among them, the vocabulary on the designation of the highest God’s people of the Christian religion, names of religious holidays, posts, memorable days is represented most quantitatively, as well as the vocabulary reflecting the organizational life of the church as an establishment and public institution (names of clergy posts, holy dignitaries, items of church use). Onomastic vocabulary and vocabulary on the designation of religious holidays are usually used in canonical forms, thus folk forms and newly created words are witnessed. Medical vocabulary is widely presented.A specific feature of the researched journal is that popular science materials about volyn shrines – icons are being constantly published in it, using a special art terminology.In religious periodical issues of political, social, economic, cultural life are actively discussed which determines the use of vocabulary denoting these realities.Religious periodicals, as well as secular media editions, demonstrate processes of a spoken vocabulary active usage.
The goal of this special issue is to highlight some exemplary ways in which the digital humanities are being applied to the study of classical Chinese literature. By digital humanities we mean methods of humanistic inquiry assisted by digital sources and tools. The articles in this issue cover a wide range of such sources and methods, but rather than focus on theory or methodology, they provide concrete case studies that offer new insights driven by digital tools and databases. These articles do not just promise to open up new avenues of inquiry but represent tangible efforts to make good on that promise. They put forth bold conclusions about the history of traditional Chinese literary culture, showing how these technologies can help support and extend the traditional concerns of philology and literary studies: to reexamine classical literary texts within the contexts of their production, reception, and circulation.The digital humanities, simply put, are the humanities aided by computers. Nearly all literary scholars now access research digitally, scour source texts in massive online corpora, collaborate via e-mail and cloud-based word processors, and use online technologies to distribute their work. Digital tools are already a pervasive part of nearly all forms of scholarship.Of course, some humanistic studies are more firmly rooted in the digital than others. We generally apply the term digital humanities to works that use one or more computing technologies as a methodological cornerstone. These often involve some amount of data modeling and quantitative analysis. Though often couched in the language of innovation, such approaches do not mark a sharp break with earlier methods. The history of systematically sorting, counting, and analyzing literary texts is long. In sinophone academia, it begins at least as far back as the large-scale kaozheng 考證 (evidential studies) scholarship of the Qing dynasty (1644–1911); can be traced through early twentieth-century reformers' obsession with numbers, charts, and graphs; and continues to the present.1 In anglophone academia, digital approaches to literary texts, such as stylometry, distant reading, bibliography, and literary sociology and geography, can claim a similarly venerable pedigree.2The digital humanities, like their predecessors, cobble together an amalgam of methods to advance new claims about humanistic topics. Like other humanists, practitioners of the digital humanities tend to be self-critical about their methods: we understand that the very framing of a question shapes the answer, that data modeling is itself an act of interpretation, and that computer-assisted analysis becomes meaningful only with human intervention. The digital humanities do not disrupt previous generations of humanistic inquiry; they support and extend them.Digital technologies also allow scholars to more clearly present the process of their research. Like the older technology of the footnote, online repositories let one check the sources underlying a claim, trace its steps, and reexamine its conclusions if necessary.3 To this end, we have created a data repository for each of the articles in this issue, where readers may download original data sets, technical appendices, and related documentation (see www.chinesepoetryforum.org/?page_id=1512). In this way, we attempt to model openness in our scholarship—a practice that is already ascendant in some circles, and one that we hope may become ubiquitous.Our first article comes from Donald Sturgeon, the founder and curator of one of the best-known textual databases in the field, the Chinese Text Project (ctext.org). In his article, Sturgeon describes how to find patterns of “text reuse” throughout early Chinese texts. His method is designed to help identify highly similar word usage and direct borrowings between discrete passages and to discover more amorphous forms of intertextual relationships. Sturgeon's examples, primarily drawn from the Mozi 墨子, include both detection of specific, small-scale textual parallels and calculation of overall lexical similarity between chapters—a method for algorithmic identification of parallels that can be applied to any text or corpora. In the second half of the article, he provides a detailed comparison between several text reuse metrics (cosine similarity and term frequency–inverse document frequencyweighting) and his “n-gram overlap” method, with the end goal of being to be able to identify and interrogate highly similar lexical elements even when they occur within radically different rhetorical structures. Computational techniques, Sturgeon concludes, are efficient when it comes to performing large-scale data-driven tasks; these practices are useful for identifying potential correlations, and then it is up to the experts to interpret their significance and causes. His new methodology and digital tool kit for identifying text reuse will prove invaluable to scholars seeking to make sense of early Chinese textual relationships on a large scale.The next article, by Evan Nicoll-Johnson, analyzes text reuse in one of its traditional forms: annotation. Specifically, he focuses on shared bibliographic notes in two important texts from the fifth and sixth centuries, the Sanguozhi 三國志 (Record of the Three Kingdoms) and Shishuo xinyu 世說新語 (New Account of Tales of the World). By creating a citation network from these notes, Nicoll-Johnson shows the extent to which these two very different texts emerged from a shared bibliographic environment. The early medieval period saw a surge in access to books, and historiographers drew extensively from this new resource to write their histories. By representing these texts' notes as a network, Nicoll-Johnson not only sheds light on the multipolar relations between many texts but also provides a new visual metaphor for the production and circulation of such texts. In this way, early medieval texts should be understood not as discrete units but as composites of dozens or hundreds of shared passages. This kind of argument, about the blurry boundaries between original texts and compendia, had been made in the predigital era, but only recent technology allows it to be so powerfully articulated and deeply felt in a visual form.The next four articles offer various takes on the celebrated poetry of the Tang dynasty (618–907). The boldest, methodologically speaking, is Mariana Zorkina's article on “poems on things” (yongwu shi 詠物詩). Zorkina uses distributional semantics and neural networks to describe common correlations between words, lexical strings, and whole poems in this popular verse genre. Her work is valuable because it describes with precision the baseline of Tang poetic discourse. If individual style, as some claim, is deviation from a norm,4 then the norms limned by Zorkina and her algorithm are crucial for understanding the signature achievements of Li Bai 李白 (701–62), Du Fu 杜甫 (712–70), and dozens of other beloved poets. Additionally, Zorkina's article points to deeper, unexpected congruencies. There is a strong correlation, for example, between the usage of tiger (hu 虎) and happiness (xi 喜) in poems on things. This is not because tigers bring joy but, rather, because both terms partake of shared linguistic modes governing poetic expression. Zorkina's research thus demonstrates how computers can highlight previously unseen patterns that close reading can then explicate.Chao-lin Liu (with Mazanec and Tharsen) also proposes ways of understanding the macroscopic patterns of Tang poetry. The main purpose of Liu et al.'s article is to introduce readers to the possible applications of a textual algorithm Liu developed, FindCommon, to the study of classical Chinese poetry. His tool is not just an improved version of the classic concordance or word search—the basic functions one might expect from such an algorithm. It goes further by offering a window on word co-occurrences, individual poets' styles, quotation and allusion, the social relations between poets, and diachronic changes in the poetic tradition. Liu's program even goes so far as to highlight limitations in the textual sources and their digital analysis, such as multiple authorial attributions to a single poem in Quan Tang shi 全唐詩 (Complete Poems of the Tang Dynasty)—his algorithm points to gaps in the archive that require further philological investigation to resolve.Thomas J. Mazanec's article takes a deep dive into one of Liu's concerns, the relations between poets as asserted in their works. In search of a new take on literary history that does not privilege a few emblematic texts, his article attempts to reconstruct the late Tang poetic world as a vast network of imagined literary relations. Combining social-network analysis with close readings, the article concludes that mobility became increasingly important to a poet's place in the network as the Tang collapsed in the late ninth century. This, in turn, calls attention to the centrality of figures normally marginalized in Tang literary history, such as Jia Dao 賈島 (779–843) and Buddhist poet-monks. By using the framework of the network, the “dynamic literary history” Mazanec proposes sees poets not as static icons but as actors who move between genres, modes, styles, cliques, and locations.Location is the main topic of Wang Zhaopeng and Qiao Junjun's article (translated by Mazanec), which looks at the geographic distribution of the Tang poetic world. Using a meticulously curated database of poets' hometowns and the places they traveled, Wang and Qiao reach several important conclusions about the geography of Tang poetry. First, northern cities produced more poets until the late Tang (835–907), when they began to be outnumbered by southerners. Second, no matter where poets were from, most poetry was written in the south, meaning that much of it was written by northern poets while traveling far from home. Third, the two capitals of the Tang, Chang'an 長安 and Luoyang 洛陽, held by far the greatest appeal for poets; nevertheless, poetry was produced everywhere, even in undeveloped backwaters and remote provinces. By focusing on geography, Wang and Qiao highlight the conditional nature of Tang poetry: the vast majority was produced in response to specific circumstances, on specific occasions, in specific places. Tang poems are ephemera as much as they are monuments, and that very ephemerality is one source of their power.Ephemera become monuments through canonization, and canonization is precisely the subject of Timothy Clifford's article. With his contribution, we jump ahead six centuries to see how later anthologists made sense of the classical literary tradition. Clifford's network analysis of the contents of sixteenth- and seventeenth-century “ancient-style prose” (guwen 古文) anthologies argues that such collections represent successive attempts to overturn the canon of model examination essays. He offers a new take on Ming literary history by demonstrating that the debate over prose was organized not around an imitative-expressionist dichotomy but around proposals of new canons entirely: Qin-Han dynasty prose, transdynastic prose, and xiaopin 小品 (informal essays). The last of these, which more strongly emphasized the contributions of women, was purely an invention of seventeenth-century printers and had no precedent in earlier eras. Like many articles in this special issue, Clifford uses digital methods to provide a broad framework for his analysis and then digs deep into his source material (here, prefaces to the anthologies) to develop that framework into a strong thesis. In so doing, he provides a new model for the ways that Ming anthologists collected, sorted, and debated over the increasingly unwieldy classical literary tradition.Our final article, by Huang Yi-long and Bingyu Zheng, also grapples with the enormity of the classical Chinese textual tradition. It introduces to an English-language audience a method Huang developed over several decades called “electronic textual research” (e-kaoju, abbreviated ETR) that uses digital tools to advance traditional philological inquiry. Huang and Zheng demonstrate the usefulness of this method to the study of eighteenth-century literature by uncovering obscure literary allusions used in poems and by determining identities and relationships of people in the social circle of Cao Xueqin 曹雪芹 (c. 1715–63), author of the celebrated novel Dream of the Red Chamber (Honglou meng 紅樓夢). In their painstakingly researched examples, Huang and Zheng show that digital tools allow for greater depth as well as greater breadth of analysis. ETR is proof that digital literary studies is not limited to distant reading; it enhances close reading, too.Huang and Zheng's article, which demonstrates the continuity of digital technology with philological methods, nicely summarizes the general contributions of this special issue. Rather than focus on developing new tools for their own sake, these articles emphasize the way new tools and methods can support and extend long-standing practices of humanistic inquiry, such as the interpretation of classical literature in the contexts of its production, circulation, and reception. In this way, we envision a future in which the digital humanities have been normalized. We predict that digital sources and methods will no longer constitute their own field; instead, they will become part of a methodological tool kit familiar to any humanist. New generations of sinologists, who will learn programming languages alongside Japanese and French, will employ word vectors and network graphs as approaches to Chinese literature in conjunction with close reading and manuscript analysis. There will not be a divide between “digital” and other scholars. Articles will primarily be judged by the content of argument, not the medium of their analysis. The future of the digital humanities, in short, is their own erasure.The articles collected in this special issue were first presented February 9–10, 2018, at the University of California, Santa Barbara, for the conference “Patterns and Networks in Classical Chinese Literature: Notes from the Digital Frontier.” We would like to thank the University of California, Santa Barbara, for hosting us and for the generous support of its Interdisciplinary Humanities Center, College of Letters and Science, Humanities and Fine Arts, Center for Taiwan Studies, East Asia Center, Center for Information Technology and Society, and Departments of East Asian Languages and Cultural Studies, Comparative Literature, Linguistics, and History, as well as from the Forum on Chinese Poetic Culture.As guest editors of this special issue, we express our gratitude to the anonymous readers for every article in this issue, who have provided constructive and detailed suggestions and comments. We also thank Xiaohui Zhang for providing editorial assistance for this special issue.Finally, we would like to thank the general editors, Zong-qi Cai and Yuan Xingpei, for their tireless efforts and for giving us the opportunity to put this collection together.
Design and implementation of automatic evaluation methods is an integral part of any scientific research in accelerating the development cycle of the output. This is no less true for automatic machine translation (MT) systems. However, no such global and systematic scheme exists for evaluation of performance of an MT system. The existing evaluation metrics, such as BLEU, METEOR, TER, although used extensively in literature have faced a lot of criticism from users. Moreover, performance of these metrics often varies with the pair of languages under consideration. The above observation is no less pertinent with respect to translations involving languages of the Indian subcontinent. This study aims at developing an evaluation metric for English to Hindi MT outputs. As a part of this process, a set of probable errors have been identified manually as well as automatically. Linear regression has been used for computing weight/penalty for each error, while taking human evaluations into consideration. A sentence score is computed as the weighted sum of the errors. A set of 126 models has been built using different single classifiers and ensemble of classifiers in order to find the most suitable model for allocating appropriate weight/penalty for each error. The outputs of the models have been compared with the state-of-the-art evaluation metrics. The models developed for manually identified errors correlate well with manual evaluation scores, whereas the models for the automatically identified errors have low correlation with the manual scores. This indicates the need for further improvement and development of sophisticated linguistic tools for automatic identification and extraction of errors. Although many automatic machine translation tools are being developed for many different language pairs, there is no such generalized scheme that would lead to designing meaningful metrics for their evaluation. The proposed scheme should help in developing such metrics for different language pairs in the coming days.
Older adults tend to suffer a decline in some of their cognitive capabilities, being language one of least affected processes. Word association norms (WAN) also known as free word associations reflect word-word relations, the participant reads or hears a word and is asked to write or say the first word that comes to mind. Free word associations show how the organization of semantic memory remains almost unchanged with age. We have performed a WAN task with very small samples of older adults with Alzheimer’s disease (AD), vascular dementia (VaD) and mixed dementia (MxD), and also with a control group of typical aging adults, matched by age, sex and education. All of them are native speakers of Mexican Spanish. The results show, as expected, that Alzheimer disease has a very important impact in lexical retrieval, unlike vascular and mixed dementia. This suggests that linguistic tests elaborated from WAN can be also used for detecting AD at early stages.
The divergence of actual spoken usage from the prescriptive Croatian accentual norm has been widely noted, but such observations are largely impressionistic. Relatively little acoustic data is available for the realization of lexical prosodic features specifically in Croatian, as opposed to other closely related varieties, and previous studies have focused mainly on measurements of isolated forms produced by "model" speakers, chosen specifically for their ability to reproduce the standard accentuation. The current study analyzes samples of connected speech taken from recordings of the program <i>Govorimo hrvatski</i> on Croatian Radio 1, comparing the results to those in previous acoustic studies of Croatian or Serbian accentuation. The implications of these findings for the viability of the current prescriptive norm are considered within the Croatian sociolinguistic context.
Evaluation is crucial in the research and development of automatic summarization applications, in order to determine the appropriateness of a summary based on different criteria, such as the content it contains, and the way it is presented. To perform an adequate evaluation is of great relevance to ensure that automatic summaries can be useful for the context and/or application they are generated for. To this end, researchers must be aware of the evaluation metrics, approaches, and datasets that are available, in order to decide which of them would be the most suitable to use, or to be able to propose new ones, overcoming the possible limitations that existing methods may present. In this article, a critical and historical analysis of evaluation metrics, methods, and datasets for automatic summarization systems is presented, where the strengths and weaknesses of evaluation efforts are discussed and the major challenges to solve are identified. Therefore, a clear up-to-date overview of the evolution and progress of summarization evaluation is provided, giving the reader useful insights into the past, present and latest trends in the automatic evaluation of summaries.
We introduce Picturebook, a large-scale lookup operation to ground language via 'snapshots' of our physical world accessed through image search. For each word in a vocabulary, we extract the top-k images from Google image search and feed the images through a convolutional network to extract a word embedding. We introduce a multimodal gating function to fuse our Picturebook embeddings with other word representations. We also introduce Inverse Picturebook, a mechanism to map a Picturebook embedding back into words. We experiment and report results across a wide range of tasks: word similarity, natural language inference, semantic relatedness, sentiment/topic classification, image-sentence ranking and machine translation. We also show that gate activations corresponding to Picturebook embeddings are highly correlated to human judgments of concreteness ratings.
This article presents a comparison of different Word Sense Induction (wsi) clustering algorithms on two novel pseudoword data sets of semantic-similarity and co-occurrence-based word graphs, with a special focus on the detection of homonymic polysemy. We follow the original definition of a pseudoword as the combination of two monosemous terms and their contexts to simulate a polysemous word. The evaluation is performed comparing the algorithm’s output on a pseudoword’s ego word graph (i.e., a graph that represents the pseudoword’s context in the corpus) with the known subdivision given by the components corresponding to the monosemous source words forming the pseudoword. The main contribution of this article is to present a self-sufficient pseudoword-based evaluation framework for wsi graph-based clustering algorithms, thereby defining a new evaluation measure (top2) and a secondary clustering process (hyperclustering). To our knowledge, we are the first to conduct and discuss a large-scale systematic pseudoword evaluation targeting the induction of coarse-grained homonymous word senses across a large number of graph clustering algorithms.
One of the most demanded types of text in higher education is argumentation, present in different discursive genres such as the academic essay. Therefore, becoming familiarized with the features and structures of argumentative texts and the different kinds of arguments is essential to perform in the different disciplines. On the other hand, research shows that collaborative writing, peer review and the use of rubrics improve the quality of written productions in different educational levels.\nThe objective of this paper is to present the impact of a rubric for the evaluation of argumentative texts in higher education. We will report on the process of elaboration of the rubric (creation, validation and pilot experiment) and its dimensions (content, argumentation and persuasion, coherence and cohesion, and use of the linguistic norm), as well as the results about the students’ perception of its use. Results show the students’ positive response and the tool’s potential. We find it convenient to elaborate rubrics for the different types of text and discursive academic genres in higher education.
In this paper, we present an approach for automatically creating a combinatory categorial grammar (CCG) treebank from a dependency treebank for the subject–object–verb language Hindi. Rather than a direct conversion from dependency trees to CCG trees, we propose a two stage approach: a language independent generic algorithm first extracts a CCG lexicon from the dependency treebank. An exhaustive CCG parser then creates a treebank of CCG derivations. We also discuss special cases of this generic algorithm to handle linguistic phenomena specific to Hindi. In doing so we extract different constructions with long-range dependencies like coordinate constructions and non-projective dependencies resulting from constructions like relative clauses, noun elaboration and verbal modifiers.
Most research into cognitive biases has used Western samples, despite potential East-West socio-cultural differences. One reason is the lack of appropriate measures for non-Westerners. This study is about cross-linguistic equivalence which needs to be established before assessing cross-cultural differences in future research. We developed parallel Mandarin and English measures of interpretation bias and attention bias using back-translation and decentering procedures. We assessed task equivalence by administering both sets of measures to 47 bilingual Mandarin-English speakers. Interpretation bias measurement was similar and reliable across language versions, confirming suitability of the Mandarin versions for future cross-cultural research. By contrast, scores on attention bias tasks did not intercorrelate reliably, suggesting that nonverbal stimuli such as pictures or facial expressions of emotion might present better prospects for cross-cultural comparison. The development of the first set of equivalent measures of interpretation bias in an Eastern language paves the way for future research investigating East-West differences in biased cognition.
We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that transforms any input word vector space to emphasise the asymmetric relation of lexical entailment (LE), also known as the IS-A or hyponymy-hypernymy relation. By injecting external linguistic constraints (e.g., WordNet links) into the initial vector space, the LE specialisation procedure brings true hyponymyhypernymy pairs closer together in the transformed Euclidean space. The proposed asymmetric distance measure adjusts the norms of word vectors to reflect the actual WordNetstyle hierarchy of concepts. Simultaneously, a joint objective enforces semantic similarity using the symmetric cosine distance, yielding a vector space specialised for both lexical relations at once. LEAR specialisation achieves state-of-the-art performance in the tasks of hypernymy directionality, hypernymy detection, and graded lexical entailment, demonstrating the effectiveness and robustness of the proposed asymmetric specialisation model.