Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
18265 papers
The large communication cost for exchanging gradients between different nodes significantly limits the scalability of distributed training for large-scale learning models. Motivated by this observation, there has been significant recent interest in techniques that reduce the communication cost of distributed Stochastic Gradient Descent (SGD), with gradient sparsification techniques such as top-k and random-k shown to be particularly effective. The same observation has also motivated a separate line of work in distributed statistical estimation theory focusing on the impact of communication constraints on the estimation efficiency of different statistical models. The primary goal of this paper is to connect these two research lines and demonstrate how statistical estimation models and their analysis can lead to new insights in the design of communication-efficient training techniques. We propose a simple statistical estimation model for the stochastic gradients which captures the sparsity and skewness of their distribution. The statistically optimal communication scheme arising from the analysis of this model leads to a new sparsification technique for SGD, which concatenates random-k and top-k, considered separately in the prior literature. We show through extensive experiments on both image and language domains with CIFAR-10, ImageNet, and Penn Treebank datasets that the concatenated application of these two sparsification methods consistently and significantly outperforms either method applied alone.
Contextual information can modulate the conditioned response to a threat signal (conditioned stimulus, CS+): fear responses are either potentiated or attenuated depending on whether the context is threatening or safe. In this study, we investigated the influence of context on conditioned fear as well as on generalization of conditioned fear. Thirty-two participants underwent a cue-in-context learning protocol in virtual reality (VR). On Day 1 (acquisition), participants received a mild painful electric shock (unconditioned stimulus, US) in one virtual room (fear context, CTX+) at the offset of one colored light (CS+), but never at the offset of another colored light (CS-). In a second room (safety context, CTX-), the two lights were also presented, but not the US. Successful cue conditioning was indicated by aversive ratings and startle potentiation but not skin conductance responses (SCR) to CS+ versus CS- in CTX+ and not in CTX-. On Day 2 (generalization), participants re-visited both fear and safety contexts plus a generalization context (G-CTX), which was an equal mix of CTX+ and CTX-. The two CSs were shown again in all three contexts. Generalization of conditioned fear was revealed in affective ratings (CS+ was rated more aversive than CS- in G-CTX), but not in physiological measures (equal startle potentiation to CS+ versus CS- in all contexts). In sum, contextual information modulates the responses to a threat signal such that a safety context can inhibit conditioned fear. Interestingly, generalization processes also depend on contextual information.
In this paper, we introduce the first Universal Dependencies (UD) treebank for standard Albanian, consisting of 60 sentences collected from the Albanian Wikipedia, annotated with lemmas, universal part-of-speech tags, morphological features and syntactic dependencies. In addition to presenting the treebank itself, we discuss a selection of linguistic constructions in Albanian whose analysis in UD is not self-evident, including core arguments and the status of indirect objects, pronominal clitics, genitive constructions, prearticulated adjectives, and modal verbs.
This paper demonstrates how multilingual adolescents initiate language-directed teasing in family interaction and thus contribute to reinforcing or challenging social hierarchies and norms in the family. It investigates the case of a multilingual family living in Northern Norway (two parents and five children ranging from 3 to 18 years old). To a varying extent, and with varying degrees of competence, all family members use three languages in their daily lives: English, Spanish, and Norwegian. The data consists of self-recorded material of family interactions (9 h; 549 min) that were collected over the course of one year. A close interactional analysis shows how the siblings target linguistic production in teasing attacks, and use language-directed teasing as an interactional resource to position themselves and their family members. Drawing on Billig's (2005) theory of disciplinary humor, the article argues that playful corrections of perceived norm transgressions may be understood as situated (re)production and negotiation of social and linguistic norms, through which the young family members participate in the construction of the family as a community of practice.
Affect fluctuates in a moment-to-moment fashion, reflecting the continuous relationship between the individual and the environment. Despite substantial research, there remain important open questions regarding how a stream of sensory input is dynamically represented in experienced affect. Here, approaching affect as a temporally dependent process, we show that momentary affect is shaped by a combination of the affective impact of stimuli (i.e., visual images for the current studies) and previously experienced affect. We also found that this temporal dependency is influenced by uncertainty of the affective context. Participants in each trial viewed sequentially presented images and subsequently reported their affective experience, which was modeled based on images' normative affect ratings and participants' previously reported affect. Study 1 showed that self-reported valence and arousal in a given trial is partly shaped by the affective impact of the given images and previously experienced affect. In Study 2, we manipulated context uncertainty by controlling occurrence probabilities for normatively pleasant and unpleasant images in separate blocks. Increasing context uncertainty (i.e., random occurrence of pleasant and unpleasant images) was associated with increased negative affect. In addition, the relative contribution of the most recent image to experienced pleasantness increased with increasing context uncertainty. Taken together, these findings provide clear behavioral evidence that momentary affect is a temporally dependent and continuous process, which reflects the affective impact of recent input variables and the previous internal state, and that this process is sensitive to the affective context and its uncertainty. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Keywords play a major role in representing the gist of a document. Therefore, a lot of Natural Language processing tools have been implemented to identify keywords in both structured and unstructured texts. Text that appears in social media platforms such as twitter is mostly unstructured because of the character limitation. Consequently, a lot of short terms and symbols such as emoticons and URLs are included in tweets. Keyword extraction from grammatically ambiguous text is not easy compared to structured text since it is hard to rely on the linguistic features in unstructured texts. But when it comes to news on twitter, it may contain somewhat structured text than informal text does but it depends on the tweeter, the person who posts the tweet. In this paper, a methodology is proposed to extract keywords from a given tweet to retrieve relevant news that has been posted on twitter, for fake news detection. The intention of extracting keywords is to find more related news efficiently and effectively. For this approach, a corpus that contains tweet texts from different domains is built in order to make this approach more generic instead of making it a domain-specific approach. In fact, the Stanford Core NLP tool kit, Wordnet linguistic database and statistical method are used for extracting keywords from a tweet. For the system evaluation, the Turing test which has human intervention is used. The system was able to acquire an accuracy of 67.6% according to the evaluation conducted.
We develop an approach to growing deep network architectures over the course of training, driven by a principled combination of accuracy and sparsity objectives. Unlike existing pruning or architecture search techniques that operate on full-sized models or supernet architectures, our method can start from a small, simple seed architecture and dynamically grow and prune both layers and filters. By combining a continuous relaxation of discrete network structure optimization with a scheme for sampling sparse subnetworks, we produce compact, pruned networks, while also drastically reducing the computational expense of training. For example, we achieve $49.7\%$ inference FLOPs and $47.4\%$ training FLOPs savings compared to a baseline ResNet-50 on ImageNet, while maintaining $75.2\%$ top-1 accuracy -- all without any dedicated fine-tuning stage. Experiments across CIFAR, ImageNet, PASCAL VOC, and Penn Treebank, with convolutional networks for image classification and semantic segmentation, and recurrent networks for language modeling, demonstrate that we both train faster and produce more efficient networks than competing architecture pruning or search methods.
The exponentially evolving size of information today has made it difficult to find relevant information quickly and efficiently. A good extractive text summarizer not only provides the most significant information from the document but also helps the user to decide the relevance of the information. The proposed method is a knowledge-based, generic, extractive text summarization technique. Our approach is based on the centrality of a sentences in the graphical representation of the documents. The graph is constructed using the pair-wise soft-cosine similarity measures between the sentences derived using the S semantic relations presented in WordNet lexical database. Eigenvector centrality measure outperforms the weighted degree, betweenness and closeness centrality measures. The resultant summary is compared against the gold-standard summaries of BBC news articles from year 2004 to 2005 and DUC 2007 datasets. The ROUGE-I, -2 and -Lmetrices are used to evaluate the results and found that our approach performs better than LexRank, TextRank, Luhn and LSA baseline text summarizers.
Abstract The presentation of visual food cues (e.g., food plating) can affect our appetite and leads to characteristic changes of early as well as late positivity in the electroencephalogram. The present event-related potential (ERP) study attempted to change ERPs and affective ratings for food pictures by rearranging the components of a depicted meal (conventional presentation) as a smiley or frowny. The images were presented to 68 women (mean age = 24 years), who rated the wanting and liking of the meals. Compared to conventional food plating, smiley and frowny meals elicited enhanced amplitudes of the P200, P300, and late positive potential (LPP) in a large occipito-parietal cluster. Frowny meals were rated as less appetizing than conventional food presentations. The mentioned ERP components are concomitants of face configuration processing (P200), automatic attention/novelty detection (P300), and voluntary attention/assignment of emotional meaning (LPP). Thus, the combination of two affective cues (food, face) in one stimulus changed the activation in motivational circuits of the brain. Also, serving a meal as a frowny could help to regulate appetite.
Abstract Current theories of risk perception point to the powerful role of emotion and the neglect of probabilistic information in the face of risk, but these tendencies differ across individuals. We propose a method for measuring individuals' emotional sensitivity to probability to assess how feelings about probabilities, rather than the probabilities themselves, influence decisions. Participants gave affective ratings (worry or excitement) to 14 risky events, each with a specified probability ranging from 1 in 10 to 1 in 10,000,000. For each participant, we regressed these emotional responses against item probabilities, estimating a slope (the degree to which emotional responses change with probability) and an intercept (the emotional reaction to an event with a fixed probability). These two parameters were treated as individual difference scores and included in models predicting reactions to several health risk scenarios. Both emotional sensitivity to probability (slope) and emotional reactivity to possibility (intercept) significantly predicted responses to these scenarios, above and beyond the predictive power of other well‐established individual difference measures.
Using data from the World Atlas of Language Structures and the Universal Dependencies treebanks, we provide converging evidence from linguistic typology and comparative corpus linguistics for an efficiency-based trade-off in the encoding of referentially accessible subjects. Specifically, when familiar subjects are marked as bound elements attaching to the verb, the chances of having obligatory independent subject pronouns decrease significantly across the world's languages. At the same time, there is a trend against not encoding the subject at all, leading us to postulate an overall tendency to encode familiar subjects once and only once in a neutral topiccomment utterance. This tendency is mirrored in more fine-grained corpus data from Slavic: East Slavic languages, in contrast to the other members of the genus, have past forms without verbal subject encoding, and it is precisely with these (former participle) forms that the use of independent subject pronouns is significantly higher than with other, non-participial verb forms. By contrast, the occurrence of independent subject pronouns does not differ across various verb forms in other Slavic languages, as none of them has been affected by a loss of verbal subject encoding.
Advertising can be regarded as a means of persuading consumers to take action with respect to products, ideas or services. It is a complex phenomenon because it involves two interacting processes, namely, communication and persuasion, and both processes are many-faceted. When advertisers intend to make the advertisement language creative and inventive, they use language different from the linguistic norm. Being unconventional, the advertisements which adopt deviation can give the readers unexpected surprise and make a strong impression on their mind. This kind of language phenomenon is technically called linguistic deviation. Then based on some scholars’ classifications, the author classifies different deviations in advertising (deviation of phonology, graphology, lexicon, and grammar) and illustrates each deviation with some typical examples.
In this paper, a novel structured pruning approach for learning efficient long short-term memory (LSTM) network architectures is proposed. More specifically, the eigenvalues of the covariance matrix associated with the responses of each LSTM layer are computed and utilized to quantify the layers' redundancy and automatically obtain an individual pruning rate for each layer. Subsequently, a Geometric Median based (GM-based) criterion is used to identify and prune in a structured way the most redundant LSTM units, realizing the pruning rates derived in the previous step. The experimental evaluation on the Penn Treebank text corpus and the large-scale YouTube-8M audio-video dataset for the tasks of word-level prediction and visual concept detection, respectively, shows the efficacy of the proposed approach <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.
The article examines the problem of the nomination of regional vocabulary, to a certain extent reflecting the peculiarities of the life of the inhabitants of a particular region. The relevance of the work is due to the heightened interest in the study of territorially marked linguistic units operating in cities in recent decades. Their study is extremely important not only for linguistic regional studies, but also for determining the peculiarities of the development of a literary language that actively interacts with regions. In this regard, the problem of designating the linguistic units of the regiolect arises, which has not yet been fully solved. The heterogeneous nature of the regiolects (dialects, geographically marked vernaculars, literary linguistic units, local borrowings), contradictory interpretation of the linguistic norm, ignorance of the systemic nature of terminology complicate the aforementioned problem. The purpose of the article is to determine the most appropriate term to denote territorially marked vocabulary. To achieve this goal, the following research methods were used: - theoretical - analysis and synthesis of linguistic literature on the research problem; - comparative-comparative - comparison of terms used to denote regional vocabulary, identifying the degree of their consistency and adequacy to the nominated object. In the course of the work done, the following results were obtained. An extraordinary inconsistency in the terminological naming of territorially marked vocabulary is revealed. The advantages and disadvantages of each term are established, taking into account the accuracy of the designated object and the systemic nature of the terminology. The term is defined that most adequately reflects the essence of the designated object and is organically included in the system of linguistic terminology. The relation of regionalisms to general literary vocabulary is revealed and their non-standardization is proved. The inconsistency of identifying the regional linguistic norm and the regional variant of the literary language has been substantiated.
Language games represent one of the most fascinating challenges of research in Artificial Intelligence. One of the problems that lies in this set is the development of an automated solution generator for Crossword puzzles. Automatic resolution of crossword puzzles is an open natural language challenge that demands the filling of puzzle grid with candidate answers, while satisfying the grid constraints. Moreover, providing an accurate list of answer candidates has a major impact on the performance of the automatic crossword resolution task. This paper proposes a semantic-based search approach for generating candidate answer lists for definition type clues by taking advantage of the lexical relations encoded in WordNet, the lexical database for English.
Traditional NLP has long held (supervised) syntactic parsing necessary for\nsuccessful higher-level semantic language understanding (LU). The recent advent\nof end-to-end neural models, self-supervised via language modeling (LM), and\ntheir success on a wide range of LU tasks, however, questions this belief. In\nthis work, we empirically investigate the usefulness of supervised parsing for\nsemantic LU in the context of LM-pretrained transformer networks. Relying on\nthe established fine-tuning paradigm, we first couple a pretrained transformer\nwith a biaffine parsing head, aiming to infuse explicit syntactic knowledge\nfrom Universal Dependencies treebanks into the transformer. We then fine-tune\nthe model for LU tasks and measure the effect of the intermediate parsing\ntraining (IPT) on downstream LU task performance. Results from both monolingual\nEnglish and zero-shot language transfer experiments (with intermediate\ntarget-language parsing) show that explicit formalized syntax, injected into\ntransformers through IPT, has very limited and inconsistent effect on\ndownstream LU performance. Our results, coupled with our analysis of\ntransformers' representation spaces before and after intermediate parsing, make\na significant step towards providing answers to an essential question: how\n(un)availing is supervised parsing for high-level semantic natural language\nunderstanding in the era of large neural models?\n
Recently, due to the interplay between syntax and semantics, incorporating syntactic knowledge into neural semantic role labeling (SRL) has achieved much attention. Most of the previous syntax-aware SRL works focus on explicitly modeling homogeneous syntactic knowledge over tree outputs. In this work, we propose to encode heterogeneous syntactic knowledge for SRL from both explicit and implicit representations. First, we introduce graph convolutional networks to explicitly encode multiple heterogeneous dependency parse trees. Second, we extract the implicit syntactic representations from syntactic parser trained with heterogeneous treebanks. Finally, we inject the two types of heterogeneous syntax-aware representations into the base SRL model as extra inputs. We conduct experiments on two widely-used benchmark datasets, i.e., Chinese Proposition Bank 1.0 and English CoNLL-2005 dataset. Experimental results show that incorporating heterogeneous syntactic knowledge brings significant improvements over strong baselines. We further conduct detailed analysis to gain insights on the usefulness of heterogeneous (vs. homogeneous) syntactic knowledge and the effectiveness of our proposed approaches for modeling such knowledge.
This paper is concerned with semantic parsing for English as a second language (ESL). Motivated by the theoretical emphasis on the learning challenges that occur at the syntaxsemantics interface during second language acquisition, we formulate the task based on the divergence between literal and intended meanings. We combine the complementary strengths of English Resource Grammar, a linguistically-precise hand-crafted deep grammar, and TLE, an existing manually annotated ESL UD-TreeBank with a novel reranking model. Experiments demonstrate that in comparison to human annotations, our method can obtain a very promising SemBanking quality. By means of the newly created corpus, we evaluate state-of-the-art semantic parsing as well as grammatical error correction models. The evaluation profiles the performance of neural NLP techniques for handling ESL data and suggests some research directions.
We present a parsing model for projective dependency trees which takes advantage of the existence of complementary dependency annotations for a language. This is the case for Arabic with the availability of CATiB and UD treebanks. Our system performs syntactic parsing according to both annotation types jointly as a sequence of arc-creating operations following the Easy-First approach, and partially created trees for one annotation type are also available to the other as features for the score function. This method gives error reduction of 9.9% on CATiB and 6.1% on UD compared to a single-task baseline, and ablation tests show that the main contribution of this reduction is given by sharing tree representation between tasks, and not simply sharing BiLSTM layers as is usually performed in NLP multitask systems.
This paper proposes a framework for the expression of typological statements which uses realvalued logics to capture the empirical truth value (truth degree) of a formula on a given data source, e.g. a collection of multilingual treebanks with comparable annotation. The formulae can be arbitrarily complex expressions of propositional logic. To illustrate the usefulness of such a framework, we present experiments on the Universal Dependencies treebanks for two use cases: (i) empirical (re-)evaluation of established formulae against the spectrum of available treebanks and (ii) evaluating new formulae (i.e. potential candidates for universals) generated by a search algorithm.
BACKGROUND/AIM: The expectation of undergoing general anesthesia triggers fear in many individuals, and such anxiety can even exceed anxiety about surgery. The only opportunity patients usually have to express their concerns and ask questions is during a preoperative visits to their anesthesiologist. Therefore, a good anesthesiologist-patient relationship is important to reduce patients' anxiety. Achieving this end requires information on patients' attitudes and concerns regarding anesthesia. This study aimed to assess patients' knowledge, attitudes, and concerns about preoperative assessment and fear associated with anesthesia at University Hospital, Jeddah, Saudi Arabia. METHODS: This cross-sectional study used a self-administered questionnaire distributed to 399 outpatients. Data were collected on patient's characteristics, perceptions about anesthesiologists, preferences for anesthetic management, and preoperative concerns regarding anesthesia. RESULTS: Most patients thought that anesthesiologists spent only 3 years in medical school and 2 years in a residency program. Survey participants had several misconceptions about anesthesiologists' role, but it did not affect ratings of their importance. Although, the confidence of patients in anesthesiologists was high, it was significantly lower than their confidence in surgeons. The most common concern expressed by the patients was based on whether anesthesiologists had sufficient experience and qualifications. CONCLUSIONS: Discussing anesthetic forms preoperatively can help decrease patients' anxiety. More efforts should be made preoperatively to address patients' high level of fear about rare side effects and discuss common side effects they tend to ignore. Preoperative preparation must allow the anesthesiologists enough time to reassure patients about their concerns, as they obtain patients' informed consent.
Part of speech tagging is a fundamental NLP task often regarded as solved for high-resource languages such as English. Current state-of-the-art models have achieved high accuracy, especially on the news domain. However, when these models are applied to other corpora with different genres, and especially user-generated data from the Web, we see substantial drops in performance. In this work, we study how a state-of-the-art tagging model trained on different genres performs on Web content from unfiltered Reddit forum discussions. More specifically, we use data from multiple sources: OntoNotes, a large benchmark corpus with 'well-edited' text, the English Web Treebank with 5 Web genres, and GUM, with 7 further genres other than Reddit. We report the results when training on different splits of the data, tested on Reddit. Our results show that even small amounts of in-domain data can outperform the contribution of data an order of magnitude larger coming from other Web domains. To make progress on out-of-domain tagging, we also evaluate an ensemble approach using multiple single-genre taggers as input features to a meta-classifier. We present state of the art performance on tagging Reddit data, as well as error analysis of the results of these models, and offer a typology of the most common error types among them, broken down by training corpus.
Social interactions enhance human memories, but little is known about how the neural mechanisms underlying episodic memories are modulated by rewarding outcomes in social interactions. To investigate this, fMRI data were recorded while healthy young adults encoded unfamiliar faces in either a competition or a control task. In the competition task, participants encoded opponents' faces in the rock-paper-scissors game, where trial-by-trial outcomes of Win, Draw, and Lose for participants were shown by facial expressions of opponents (Angry, Neutral, and Happy). In the control task, participants encoded faces by assessing facial expressions. After encoding, participants recognized faces previously learned. Behavioral data showed that emotional valence for opponents' Angry faces as the Win outcome was rated positively in the competition task, whereas the rating for Angry faces was rated negatively in the control task, and that Angry faces were remembered more accurately than Neutral or Happy faces in both tasks. fMRI data showed that activation in the medial orbitofrontal cortex (mOFC) paralleled the pattern of valence ratings, with greater activation for the Win than Draw or Lose conditions of the competition task, and the Angry condition of the control task. Moreover, functional connectivity between the mOFC and hippocampus was increased in Win compared to Angry, and the mOFC-hippocampus functional connectivity predicted individual differences in subsequent memory performance only in Win of the competition task, but not in any other conditions of the two tasks. These results demonstrate that the memory enhancement by context-dependent social rewards involves interactions between reward- and memory-related regions.
To investigate issues that arise in the process of developing a Universal Dependency (UD) treebank for Korean and Japanese, we begin by addressing the typological characteristics of Korean and Japanese. Both Korean and Japanese are agglutinative and head-final languages. And the principle of word segmentation for both languages is different from English, which makes it difficult to apply UD guidelines. Following the typological characteristics of the two languages and the issue of UD application, we review the application of UPOS and DEPREL schemes to the two languages. The annotation principles for AUX, ADJ, DET, ADP and PART are discussed for the UPOS scheme, and the annotation principles for case, aux, iobj, and obl are discussed for the DEPREL scheme.
Self-relevant functional abnormalities and identity disorders constitute the core psychopathological components in borderline personality disorder (BPD). Evidence suggests that appraising the relevance of environmental information to the self may be altered in BPD. However, only a few studies have examined self-relevance (SR) in BPD, and the neural correlates of SR processing has not yet been investigated in this patient group. The current study sought to evaluate brain activation differences between female patients with BPD and healthy controls during SR processing. A task-based fMRI paradigm was applied to evaluate SR processing in 23 female patients with BPD and 23 matched healthy controls. Participants were presented with a set of short sentences and were instructed to rate the stimuli. The differences in fMRI signals between SR rating (task of interest) and valence rating (control task) were examined. During SR rating, participants showed elevated activations of the cortical midline structures (CMS), known to be involved in the processing of self-related stimuli. Furthermore, we observed an elevated activation of the supplementary motor area (SMA) and the regions belonging to the mirror neuron system (MNS). Using whole-brain, seed-based connectivity analysis on the task-based fMRI data, we studied connectivity of networks anchored to the main CMS regions. We found a discrepancy in the connectivity pattern between patients and controls regarding connectivity of the CMS regions with the basal ganglia-thalamus complex. These observations have two main implications: First, they confirm the involvement of the CMS in SR evaluations of our stimuli and add evidence about the involvement of an extended network including the MNS and the SMA in this task. Second, the functional connectivity profile observed in BPD provides evidence for an altered functional interplay between the CMS and the brain regions involved in salience detection and reward evaluation, including the basal ganglia and the thalamus.
The objective of this work is to build an Indonesian morphological analyzer named Aksara that conforms to the Universal Dependencies (UD), especially UD v2. Many works had developed Indonesian morphological analyzer, but as far as we know none conforms to the UD annotation guidelines. In building Aksara we use the same approach with MorphInd, another Indonesian morphological analyzer, that uses finite state compiler named Foma. Aksara has capability to perform four tasks: 1) word segmentation, 2) lemmatization, 3) POS tagging, and 4) morphological features analysis. To evaluate the quality of this tool, we used an Indonesian dependency treebank that conforms to UD v2 as the gold standard. We also compare the performance measures of Aksara with MorphInd, by mapping MorphInd output to CoNNL-U format. The experiment results show that for all the four tasks Aksara outperforms MorphInd. For word segmentation task, Aksara has accuracy of 96.9%, for lemmatization with case-sensitive it has accuracy of 94.83%, for POS tagging it has F1-score of 88.2% and finally for morphological features analysis, among 18 feature-value tags already implemented, nine tags already have F1-score more than 80%.
The LiLa: Linking Latin project aims to build a Knowledge Base of language resources for the study of Latin (corpora, digital lexicons, natural-language-processing tools), based on the Linked Open Data paradigm. In this paper, we discuss the goals and motivation of the project. In particular, we focus on the role played by the lemma as a hub node that holds together the network of linguistic information. The architecture of LiLa is therefore based on lemmas and their morphological properties. The paper illustrates the strategies used to build a collection of Latin lemmas and the first experiments to link them to a set of textual resources (Latin treebanks).
In this paper, we explore self-distillation as a means to improve statistical dependency parsing models for Dutch and German over purely supervised training. Self-distillation (Furlanello et al. 2018) trains a new student model on the output of an existing (weaker) teacher model. In contrast to most previous work on self-distillation, we perform distillation using a large, unannotated corpus. We show that in dependency parsing as sequence labeling (Spoustov´a and Spousta 2010, Strzyz et al. 2019), self-distillation plus finetuning provides large improvements over models that use supervised training. We carry out experiments on the German T¨uBa-D/Z universal dependency (UD) treebank (C¸ ¨oltekin et al. 2017) and the UD conversion of the Dutch Lassy Small treebank (Bouma and van Noord 2017). We find that self-distillation improves German parsing accuracy of a bidirectional LSTM parser from 92.23 to 94.33 Labeled Attachment Score (LAS). Similarly, on Dutch we see improvement from 89.89 to 91.84 LAS.
The chapter addresses the concept of linguistic norm in the tradition of classical grammar and rhetoric, paying special attention to activities concerning standardization processes in the Romance languages. Since a clear distinction between a prescriptive and a descriptive point of view is not given in "traditional grammar", the latter is manifested in the form of grammatical treatises which often also aimed at offering norms for "correct" language use. As a consequence thereof, our contribution will be concerned with aspects relating to the realm of the history of language sciences and, at least partially, to the history of rhetoric. The period taken into consideration ranges from Latin antiquity (Cicero, Quintilian) to the middle of the 17th century (Vaugelas). The topics to be discussed were selected with regard to the significance of the respective protagonists in the history of ideas in (Latin and) Romance language standardization.
Hoarding disorder has become an official disorder in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). Hoarding disorder affects approximately 1.5% to 5% of the general population, and there is no known literature that has examined the prevalence of hoarding disorder among homeless populations or those living in supported housing, although hoarding problems can jeopardize their housing situation. This study used the Clutter Image Rating to estimate the prevalence of possible hoarding behavior among 660 adults living in supported housing. The results indicate that 18.5% of supported housing residents had hoarding behavior, which is more than three times the prevalence reported in the general population. These results suggest that hoarding behavior and possibly hoarding disorder may be more prevalent among those with histories of homelessness and housing instability, which may be of concern because it may affect both housing and health statuses.
We use Universal Dependencies treebanks to test whether a well-known typological trade-off between word order freedom and richness of morphological marking of core arguments holds within individual languages. Using Russian and German treebank data, we show that the following phenomenon (sometimes dubbed word order freezing) does occur: those sentences where core arguments cannot be distinguished by morphological means (due to case syncretism or other kinds of ambiguity) have more rigid order of subject, verb and object than those where unambiguous morphological marking is present. In ambiguous clauses, word order is more often equal to the one which is default or dominant (most frequent) in the language. While Russian and German differ with respect to how exactly they mark core arguments, the effect of morphological ambiguity is significant in both languages. It is, however, small, suggesting that languages do adapt to the evolutionary pressure on communicative efficiency and avoidance of redundancy, but that the pressure is weak in this particular respect.
In this paper, we present our submission for subtask A of the Common Sense Validation and Explanation (ComVE) shared task. We examine the ability of large-scale pre-trained language models to distinguish commonsense from non-commonsense statements. We also explore the utility of external resources that aim to supplement the world knowledge inherent in such language models, including commonsense knowledge graph embedding models, word concreteness ratings, and text-to-image generation models. We find that such resources provide insignificant gains to the performance of fine-tuned language models. We also provide a qualitative analysis of the limitations of the language model fine-tuned to this task.
The urban sound environment is one of the layers that characterizes a city, and several methodologies are used for its assessment, including the soundwalk approach. However, this approach has been tested mainly with adults. In the work presented here, the aim is to investigate a soundwalk methodology for children, analyzing the sound environment of five different sites of Gothenburg, Sweden, from children's view-point, giving them the opportunity to take action as an active part of society. Both individual assessment of the sound environment and acoustic data were collected. The findings suggested that among significant results, children tended to rank the sound environment as slightly better when lower levels of background noise were present ( L A 90 ). Moreover, traffic dominance ratings appeared as the best predictor among the studied sound sources: when traffic dominated as a sound source, the children rated the sound environment as less good. Additionally, traffic volume appeared as a plausible predictor for sound environment quality judgments, since the higher the traffic volume, the lower the quality of the sound environment. The incorporation of children into urban sound environment research may be able to generate new results in terms of children's understanding of their sound environment. Moreover, sound environment policies can be developed from and for children.
Sentiment analysis, especially for long documents, plausibly requires methods capturing complex linguistics structures. To accommodate this, we propose a novel framework to exploit task-related discourse for the task of sentiment analysis. More specifically, we are combining the largescale, sentiment-dependent MEGA-DT treebank with a novel neural architecture for sentiment prediction, based on a hybrid TreeLSTM hierarchical attention model. Experiments show that our framework using sentiment-related discourse augmentations for sentiment prediction enhances the overall performance for long documents, even beyond previous approaches using well-established discourse parsers trained on human annotated data. We show that a simple ensemble approach can further enhance performance by selectively using discourse, depending on the document length.
Arabic dependency parsers perform poorly compared to parsers of other languages. There is little research on improving the performance of Arabic parsers. However, recent research has shown slight improvements in the performance of dependency parsers by utilizing the lexical level of a dependency treebank. To our knowledge, no previous study has studied the effect of utilizing the syntactic level. In this study, we empirically investigated the impact of varying the set of dependency relations on the performance of Arabic dependency parsers. The results were compared to those of previous studies, and showed that having an appropriate set of dependency relations could improve the performance of an Arabic dependency parser.
Summary In this work a novel framework for modeling role and task allocation in Cooperative Heterogeneous Multi‐Robot Systems (CHMRSs) is presented. This framework encodes a CHMRS as a set of multidimensional relational structures (MDRSs). This set of structure defines collaborative tasks through both temporal and spatial relations between processes of heterogeneous robots. These relations are enriched with tensors which allow for geometrical reasoning about collaborative tasks. A learning schema is also proposed in order to derive the components of each MDRS. According to this schema, the components are learnt from data reporting the situated history of the processes executed by the team of robots. Data are organized as a multirobot collaboration treebank (MRCT) in order to support learning. Moreover, a generative approach, based on a probabilistic model, is combined together with nonnegative tensor decomposition (NTD) for both building the tensors and estimating latent knowledge. Preliminary evaluation of the performance of this framework is performed in simulation with three heterogeneous robots, namely, two Unmanned Ground Vehicles (UGVs) and one Unmanned Aerial Vehicle (UAV).
In this paper we present a method for identifying and analyzing adnominal possessive constructions in 66 Universal Dependencies treebanks. We classify adpossessive constructions in terms of their morphological type (locus of marking) and present a workflow for detecting and analyzing them typologically. Based on a preliminary evaluation, the algorithm works fairly reliably in adpossessive constructions that are morphologically marked. However, it performs rather poorly in adpossessive constructions that are not marked morphologically, so-called zero-marked constructions, because of difficulties in identifying these constructions with the current annotation. We also discuss different types of variation in annotation in different treebanks for the same language and for treebanks of closely related languages. The research focuses on one well-circumscribed and universal construction in the hope of generating more interest in using UD for cross-linguistic comparison and for contributing towards developing yet more consistent annotation of constructions in the UD annotation scheme.
A study of the comprehensive reading, its importance and the basic value of an educational institution in the province of Manabí was conducted, it was investigated what is the impact on students, in which it benefits them to understand what is found in the pages of a text, how to broaden its criticality, the concentration of reading, writing and oral skills, to have a better communication and how reading influenced values, it was elaborated in a research way with the help of ICT, not practical, with the inductive method - deductive, finally, the linguistic norms, pauses, emphasis, vowel sounds that are used when reading are analyzed, such as strategies that can be used for images, graphics, mental or conceptual maps, together with teacher support inside or outside the classroom, coordinate the ways of studying the topics because each student thinks and analyzes differently.
Abstract The COVID-19 pandemic has dramatically changed the nature of our social interactions. In order to understand how protective equipment and distancing measures influence the ability to comprehend others’ emotions and, thus, to effectively interact with others, we carried out an online survey across the Italian population during the first pandemic peak. Participants were shown static facial expressions (Angry, Happy and Neutral) covered by a sanitary mask or by a scarf. They were asked to evaluate the expressed emotions as well as to assess the degree to which one would adopt physical and social distancing measures for each stimulus. Results demonstrate that, despite the covering of the lower-face, participants correctly recognized the facial expressions of emotions with a polarizing effect on emotional valence ratings found in females. Noticeably, while females’ ratings for physical and social distancing were driven by the emotional content of the stimuli, males were influenced by the “covered” condition. The results also show the impact of the pandemic on anxiety and fear experienced by participants. Taken together, our results offer novel insights on the impact of the COVID-19 pandemic on social interactions, providing a deeper understanding of the way people react to different kinds of protective face covering.
There is empirical evidence that expected yet not current affect predicts decisions. However, common research designs in affective decision-making show consistent methodological problems (e.g., conceptualization of different emotion concepts; measuring only emotional valence, but not arousal). We developed a gambling task that systematically varied learning experience, average feedback balance and feedback consistency. In Experiment 1 we studied whether predecisional current affect or expected affect predict recurrent gambling responses. Furthermore, we exploratively examined how affective information is represented on a neuronal level in Experiment 2. Expected and current valence and arousal ratings as well as Blood Oxygen Level Dependent (BOLD) responses were analyzed using a within-subject design. We used a generalized mixed effect model to predict gambling responses with the different affect variables. Results suggest a guiding function of expected valence for decisions. In the anticipation period, we found activity in brain areas previously associated with valence-general processing (e.g., anterior cingulate cortex, nucleus accumbens, thalamus) mostly independent of contextual factors. These findings are discussed in the context of the idea of a valence-general affective work-space, a goal-directed account of emotions, and the hypothesis that current affect might be used to form expectations of future outcomes. In conclusion, expected valence seems to be the best predictor of recurrent decisions in gambling tasks.
Nowadays there are a lot of modern technologies in electronic lexicography: speech synthesis technology, cross-referencing between dictionary modules, spell-checking functions, etc. The increasing availability of online information has necessitated intensive research in the area of automatic text summarization within the Natural Language Processing community. Belarusian scientists are also interested in this sphere and new lexicographical approaches for creating a linguistic database are shown in the paper. The authors present English-Belarusian-Russian electronic dictionary TechLex. This is the project of the 2 nd English Department and the Department of Software for Information Systems and Technologies of the Belarusian National Technical University. The linguistic database of the dictionary is compiled not by the traditional method of processing a large number of paper dictionaries and combining the received translations, but by sequential processing of scientific and technical English-language periodicals. While the designing the dictionary the authors have taken into account the analysis of modern electronic multilingual translation dictionaries and created a client-server application in the Java programming language. The client part of the system contains a mobile application for the Android operating system, which has been tested on tablets and smartphones with different screen diagonals. The interface of the TechLex dictionary is designed taking into account the possibility of adding new subject areas and filling them with appropriate lexical material. The main advantage of our dictionary is that it is the first technical multilingual electronic dictionary having a Belarusian version.
The robustness of text classifier based on the deep neural networks can be improved by correcting adversarial spelling mistakes. The main challenge brought by these mistakes is that it is difficult for the text classifier to recognize the words in the text correctly. Existing works to deal with these mistakes can be divided into two types: optimization of training data and reconstruction of text classification model. However, these works are not suitable for the text classification model that has been deployed, as retraining or reconstruction is always involved. To address the above problem, we propose a two-step spelling correction model, which consists of a misspelled word detector and a misspelled word correcter, referred to as DE-CO. Specifically, we use a detector to recognize misspelled words in the text, use a corrector to correct the misspelled words, and then feed the correction results into the downstream classifier. In this way, without reconstruction or retraining, the normal recognition of words by the text classifier can be guaranteed. We evaluate DE-CO on adversarial examples generated from the Stanford Sentiment Treebank (SST). The classification accuracy of the downstream classifier is improved in a variety of attack scenarios, which demonstrates that DE-CO improves the robustness of the text classifier.