Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
18265 papers
In this paper we present a novel lemmatization method based on a\nsequence-to-sequence neural network architecture and morphosyntactic context\nrepresentation. In the proposed method, our context-sensitive lemmatizer\ngenerates the lemma one character at a time based on the surface form\ncharacters and its morphosyntactic features obtained from a morphological\ntagger. We argue that a sliding window context representation suffers from\nsparseness, while in majority of cases the morphosyntactic features of a word\nbring enough information to resolve lemma ambiguities while keeping the context\nrepresentation dense and more practical for machine learning systems.\nAdditionally, we study two different data augmentation methods utilizing\nautoencoder training and morphological transducers especially beneficial for\nlow resource languages. We evaluate our lemmatizer on 52 different languages\nand 76 different treebanks, showing that our system outperforms all latest\nbaseline systems. Compared to the best overall baseline, UDPipe Future, our\nsystem outperforms it on 62 out of 76 treebanks reducing errors on average by\n19% relative. The lemmatizer together with all trained models is made available\nas a part of the Turku-neural-parsing-pipeline under the Apache 2.0 license.\n
Purpose: The purpose of this paper is to prove the importance of understanding body language to achieve the effectiveness of English language classes.
 Methodology: Literature investigation is carried out to confirm the objective of this paper.
 Results: In the teaching and learning process, effective communication between a teacher and students is the utmost importance. The failure to establish effective communication in the classroom setting will result in a deficiency of the teaching and learning process.
 Implications: It is the fact that many cues of body language are culture-specific and therefore the only way to improve the understanding of body language is by interacting with people from different cultural backgrounds so that they can share socio-cultural and linguistic norms. Thus, the experience will enrich the teacher with cross-cultural nonverbal behavior which benefits his performance in the classroom. Both teachers' and students' knowledge of non-verbal language play very significant roles in making the classroom interaction successful. Therefore, finally, a summary is presented to reconfirm the importance of integrating body language into classroom interaction.
Clinical motor and non-motor effects of deep brain stimulation (DBS) of the subthalamic nucleus (STN) in Parkinson's disease (PD) seem to depend on the stimulation site within the STN. We analysed the effects of the position of the stimulation electrode within the motor STN on subjective emotional experience, expressed as emotional valence and arousal ratings to pictures representing primary rewards and aversive fearful stimuli in 20 PD patients. Patients' ratings from both aversive and erotic stimuli matched the mean ratings from a group of 20 control subjects at similar position within the STN. Patients with electrodes located more posteriorly reported both valence and arousal ratings from both the rewarding and aversive pictures as more extreme. Moreover, posterior electrode positions were associated with a higher occurrence of depression at a long-term follow-up. This brain-behavior relationship suggests a complex emotion topography in the motor part of the STN. Both valence and arousal representations overlapped and were uniformly arranged anterior-posteriorly in a gradient-like manner, suggesting a specific spatial organization needed for the coding of the motivational salience of the stimuli. This finding is relevant for our understanding of neuropsychiatric side effects in STN DBS and potentially for optimal electrode placement.
Learning hierarchical abstractions from sequences is a challenging and open problem for recurrent neural networks (RNNs). This is mainly due to the difficulty of detecting features that span over long time distances with also different frequencies. In this paper, we address this challenge by introducing surprisal-based activation, a novel method to preserve activations and skip updates depending on encoding-based information content. The preserved activations can be considered as temporal shortcuts with perfect memory. We present a preliminary analysis by evaluating surprisal-based activation on language modeling with the Penn Treebank corpus and find that it can improve performance when compared to baseline RNNs and Long Short-Term Memory (LSTM) networks.
Transfer parsing has been used for developing dependency parsers for languages with no treebank by using transfer from treebanks of other languages (source languages). In delexicalized transfer, parsed words are replaced by their part-of-speech tags. Transfer parsing may not work well if a language does not follow uniform syntactic structure with respect to its different constituent patterns. Earlier work has used information derived from linguistic databases to transform a source language treebank to reduce the syntactic differences between the source and the target languages. We propose a transformation method where a source language pattern is transformed stochastically to one of the multiple possible patterns followed in the target language. The transformed source language treebank can be used to train a delexicalized parser in the target language. We show that this method significantly improves the average performance of single-source delexicalized transfer parsers. We also show that, in the multi-source settings, parsers trained using a concatenation of transformed source language treebanks work better when a subset of the source language treebanks is used rather than concatenating all of them or only one. However, the problem of selecting the subset of treebanks whose combination gives the best-performing parser from the set of all the available treebanks is hard. We propose a greedy selection heuristic based on the labelled attachment scores of the corresponding single-source parsers trained using the treebanks after transformation.
Composers convey emotion through music by co-varying structural cues. Although the complex interplay provides a rich listening experience, this creates challenges for understanding the contributions of individual cues. Here we investigate how three specific cues (attack rate, mode, and pitch height) work together to convey emotion in Bach's Well Tempered-Clavier (WTC). In three experiments, we explore responses to (1) eight-measure excerpts and (2) musically “resolved” excerpts, and (3) investigate the role of different standard dimensional scales of emotion. In each experiment, thirty nonmusician participants rated perceived emotion along scales of valence and intensity (Experiments 1 & 2) or valence and arousal (Experiment 3) for 48 pieces in the WTC. Responses indicate listeners used attack rate, Mode, and pitch height to make judgements of valence, but only attack rate for intensity/arousal. Commonality analyses revealed mode predicted the most variance for valence ratings, followed by attack rate, with pitch height contributing minimally. In Experiment 2 mode increased in predictive power compared to Experiment 1. For Experiment 3, using “arousal” instead of “intensity” showed similar results to Experiment 1. We discuss how these results complement and extend previous findings of studies with tightly controlled stimuli, providing additional perspective on complex issues of interpersonal communication.
Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), and Memory Networks which contain memory are popularly used to learn patterns in sequential data. Sequential data has long sequences that hold relationships. RNN can handle long sequences but suffers from the vanishing and exploding gradient problems. While LSTM and other memory networks address this problem, they are not capable of handling long sequences (50 or more data points long sequence patterns). Language modelling requiring learning from longer sequences are affected by the need for more information in memory. This paper introduces Long Term Memory network (LTM), which can tackle the exploding and vanishing gradient problems and handles long sequences without forgetting. LTM is designed to scale data in the memory and gives a higher weight to the input in the sequence. LTM avoid overfitting by scaling the cell state after achieving the optimal results. The LTM is tested on Penn treebank dataset, and Text8 dataset and LTM achieves test perplexities of 83 and 82 respectively. 650 LTM cells achieved a test perplexity of 67 for Penn treebank, and 600 cells achieved a test perplexity of 77 for Text8. LTM achieves state of the art results by only using ten hidden LTM cells for both datasets.
The lack of understanding and definitional inconsistencies regarding agritourism and the importance of cooperation in sustaining this kind of tourism are underlined in the literature. This study analyzes the perceptions of agritourism and cooperation from actors in the sector using a plurality of methods, including unsupervised (a) text mining and (b) sentiment analysis with the use of a lexical database, as well as (c) supervised qualitative data analysis. Based on the assumption that destinations with different geographic characteristics have different features and products, two different destinations as for its accessibility and tourism recognition were selected for comparison: (a) an island—Lesvos in the North Aegean Sea, and (b) a continental mountain region—Plastiras Lake, in Greece. The data were collected from personal in-depth interviews and with the use of semi-structured questionnaires. From a methodological perspective, all three methods provided unique insights on the study’s themes, and the overall image of agritourism and cooperation was positive. A common understanding seems important for cooperation and networking; however, training is needed not only for effective promotion of agritourism, but also for cooperation techniques, benefits, trust-building mechanisms and best practices.
This paper investigates which annotation scheme of dependency treebank is more congruent for the measurement of syntactic complexity and cognitive constraint of language materials. Two representatives of semantic-and syntactic-oriented annotation schemes, the Universal Dependencies (UD) and the Surface-Syntactic Universal Dependencies (SUD), are under discussion. The results show that, on the one hand, natural languages based on both annotation schemes follow the universal linguistic law of Dependency Distance Minimization (DDM); on the other hand, according to the metric of Mean Dependency Distances (MDDs), the SUD annotation scheme that accords with traditional dependency syntaxes are more expedient to measure syntactic difficulty and cognitive demand.
Topic Modeling encompasses a set of techniques for text clustering and tag recommendation with significant advantages such as unsupervised learning. Based on Latent Dirichlet Allocation (LDA) topic modeling, every single word is related to a set of topics with different weight. The weights are furt her estimated in order to determine the semantic relation between the words and the rest of the documents. Apparently the chief drawback of topic modeling techniques, specifically LDA, lies on their incapability in clustering short texts in which semantic relation between words is neglected. This issue is deemed more severe when analyzing social networks such as Twitter wherein short texts are the case. It is assumed that semantic relation between a document and the target short text helps obtain efficient clustering of short texts via topic modeling. Hence, the current paper proposes a method of topic modeling named Semantic Knowledge LDA based on semantic relations between the words in tweets from Twitter social network based on the co-occurrence of words. Additionally, we propose a method of hashtag recommender system based on topic vector (TV) text similarity, named TV based Hashtag Recommender System (TVHRS). Accordingly, we applied our word co-occurrence LDA (SKLDAC) method together with WordNet lexical database to cluster the short texts from Twitter. The clustered topics are later used as the repository for the proposed hashtag recommender system. The proposed system of both SKLDA and TVHRS were applied to a set of 12,309,911 real tweets for testing purposes. When comparing the components of the proposed system to the existing methods, we recorded higher performance in terms of precision, recall and F-Score of 0.551, 0.682 and 0.526, respectively.
Purpose This paper aims to describe the structure of an aligned Serbian-German literary corpus (SrpNemKor) contained in a digital library Bibliša. The goal of the research was to create a benchmark Serbian-German annotated corpus searchable with various query expansions. Design/methodology/approach The presented research is particularly focused on the enhancement of bilingual search queries in a full-text search of aligned SrpNemKor collection. The enhancement is based on using existing lexical resources such as Serbian morphological electronic dictionaries and the bilingual lexical database Termi. Findings For the purpose of this research, the lexical database Termi is enriched with a bilingual list of German-Serbian translated pairs of lexical units. The list of correct translation pairs was extracted from SrpNemKor, evaluated and integrated into Termi. Also, Serbian morphological e-dictionaries are updated with new entries extracted from the Serbian part of the corpus. Originality/value A bilingual search of SrpNemKor in Bibliša is available within the user-friendly platform. The enriched database Termi enables semantic enhancement and refinement of user’s search query based on synonyms both in Serbian and German at a very high level. Serbian morphological e-dictionaries facilitate the morphological expansion of search queries in Serbian, thereby enabling the analysis of concepts and concept structures by identifying terms assigned to the concept, and by establishing relations between terms in Serbian and German which makes Bibliša a valuable Web tool that can support research and analysis of SrpNemKor.
There are few studies of user interaction with music libraries comprising solely of unfamiliar music, despite such music being represented in national music information centre collections. We aim to develop a system that encourages exploration of such a library. This study investigates the influence of 69 users’ pre-existing musical genre and feature preferences on their ongoing continuous real-time psychological affect responses during listening and the acoustic features of the music on their liking and familiarity ratings for unfamiliar art music (the collection of the Australian Music Centre) during a sequential hybrid recommender-guided interaction. We successfully mitigated the unfavorable starting conditions (no prior item ratings or participants’ item choices) by using each participant’s pre-listening music preferences, translated into acoustic features and linked to item view count from the Australian Music Centre database, to choose their seed item. We found that first item liking/familiarity ratings were on average higher than the subsequent 15 items and comparable with the maximal values at the end of listeners’ sequential responses, showing acoustic features to be useful predictors of responses. We required users to give a continuous response indication of their perception of the affect expressed as they listened to 30-second excerpts of music, with our system successfully providing either a “similar” or “dissimilar” next item, according to—and confirming—the utility of the items’ acoustic features, but chosen from the affective responses of the preceding item. We also developed predictive statistical time series analysis models of liking and familiarity, using music preferences and preceding ratings. Our analyses suggest our users were at the starting low end of the commonly observed inverted-U relationship between exposure and both liking and perceived familiarity, which were closely related. Overall, our hybrid recommender worked well under extreme conditions, with 53 unique items from 100 chosen as “seed” items, suggesting future enhancement of our approach can productively encourage exploration of libraries of unfamiliar music.
Encoding and retrieval of emotionally arousing stimuli depend on the activation of multiple interconnected brain regions, with people showing differences in their individual strength of emotional perception and recollection. Understanding the association between these brain regions and the behavioral outcome might therefore have important clinical implications as dysfunctional emotional memory processes are characteristic of many psychiatric disorders. Based on behavioral and fMRI data collected from healthy young adults (N = 1'385), we investigated brain activation patterns, arousal ratings and memory performance during encoding and retrieval of negative and neutral pictures. We performed multi-voxel pattern analysis (MVPA) and voxel-wise association analyses. Subjects' individual strength of perceived arousal at encoding and subjects' memory performance at recognition could be predicted from the fMRI data of the respective tasks by using a topographically identical network of brain regions. This network was mainly left lateralized including dense clusters of voxels in the occipital and parietal lobe and including the amygdala. Voxel-wise association analyses confirmed the close link between the brain activation of both tasks and their relation to the respective behavioral outcome. These results point to the importance of the here identified brain network for emotional memory processes in health and, possibly, disease.
Abstract Generating novel design concepts is a cornerstone for producing innovative products. Although many methods have been proposed for supporting the task, their performance depends on human ability. The goal of this research is to build a method supporting designers to generate novel design concepts with the knowledge of what factors have positive effects on the novelty. Toward the goal, this research assumes that the more distant two function concepts chosen, the more novel idea would come up with by the combination of the two concepts. Based on the assumption, this paper introduces a notion of novelty potential of the combination of two function concepts, and proposes a method to assess it by the function similarity. It is calculated with the integration of a lexical database for natural language called WordNet and a distributional semantics method called word2vec. The proposed method is adapted to case studies in which students perform design concept generation for given design tasks. The correlation analysis is performed to verify the assessment performance of the proposed method. This paper discusses its possibility based on the results of the case studies.
In this paper, we present a Linguistic Informed Multi-Task BERT (LIMIT-BERT) for learning language representations across multiple linguistic tasks by Multi-Task Learning (MTL). LIMIT-BERT includes five key linguistic syntax and semantics tasks: Part-Of-Speech (POS) tags, constituent and dependency syntactic parsing, span and dependency semantic role labeling (SRL). Besides, LIMIT-BERT adopts linguistics mask strategy: Syntactic and Semantic Phrase Masking which mask all of the tokens corresponding to a syntactic/semantic phrase. Different from recent Multi-Task Deep Neural Networks (MT-DNN) (Liu et al., 2019), our LIMIT-BERT is linguistically motivated and learning in a semi-supervised method which provides large amounts of linguistic-task data as same as BERT learning corpus. As a result, LIMIT-BERT not only improves linguistic tasks performance but also benefits from a regularization effect and linguistic information that leads to more general representations to help adapt to new tasks and domains. LIMIT-BERT obtains new state-of-the-art or competitive results on both span and dependency semantic parsing on Propbank benchmarks and both dependency and constituent syntactic parsing on Penn Treebank.
Borderline personality disorder (BPD) is a diagnosis characterized by intense and labile emotion; dialectical behavior therapy, a common treatment for BPD, aims to reduce the intensity and lability of clients' emotion through multiple methods, some of which occur in the therapy session, with the expectation that changes will generalize to the rest of clients' lives. However, little research has examined how BPD clients' affect presents and varies in session or whether affect in session reflects patients' patterns of affect outside of treatment. This study had 2 aims: (a) to explore changes in clients' positive and negative affect in therapy, and (b) to assess if the severity of client psychopathology relates to affect in treatment. Positive and negative affect ratings were collected from clients (N = 73) at the start and end of every individual therapy session (total sessions = 1,474). Hierarchical linear modeling and linear regression were used to examine patterns of affect and assess the relationship between affect and severity. Results indicated that positive affect increased while negative affect decreased between the start and end of sessions, with the same pattern of change in presession affect from week to week. In addition, increased BPD severity was associated with lower presession positive affect ratings and higher negative affect ratings. Further exploration is needed to assess which dialectical behavior therapy treatment processes contribute to changes in in-session affect and how in-session affect relates to treatment outcomes. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning. Recently unsupervised lexical simplification approaches only rely on the complex word itself regardless of the given sentence to generate candidate substitutions, which will inevitably produce a large number of spurious candidates. We present a simple BERT-based LS approach that makes use of the pre-trained unsupervised deep bidirectional representations BERT. Despite being entirely unsupervised, experimental results show that our approach obtains obvious improvement than these baselines leveraging linguistic databases and parallel corpus, outperforming the state-of-the-art by more than 11 Accuracy points on three well-known benchmarks.
The paper aims to examine how the acoustic input (the surface form) and the abstract linguistic representation (the underlying representation) interact during spoken word recognition by investigating left-dominant tone sandhi, a tonal alternation in which the underlying tone of the first syllable spreads to the sandhi domain. We conducted two auditory-auditory priming lexical decision experiments on Shanghai left-dominant sandhi words with less-frequent and frequent Shanghai users, in which each disyllabic target was preceded by monosyllabic primes either sharing the same underlying tone, surface tone, or being unrelated to the tone of the first syllable of the sandhi targets. Results showed a surface priming effect but not an underlying priming effect in younger speakers who used Shanghai less frequently, but no surface or underlying priming effect in older speakers who used Shanghai more often. Moreover, the surface priming did not interact with speakers' familiarity ratings to the sandhi targets. These patterns suggest that left-dominant Shanghai sandhi words may be represented in the sandhi form in the mental lexicon. The results are discussed in the context of how phonological opacity, productivity, the non-structure-preserving nature of tone spreading, and speakers' semantic knowledge influence the representation and processing of tone sandhi words.
We describe a cross-lingual transfer method for dependency parsing that takes into account the problem of word order differences between source and target languages. Our model only relies on the Bible, a considerably smaller parallel data than the commonly used parallel data in transfer methods. We use the concatenation of projected trees from the Bible corpus, and the gold-standard treebanks in multiple source languages along with cross-lingual word representations. We demonstrate that reordering the source treebanks before training on them for a target language improves the accuracy of languages outside the European language family. Our experiments on 68 treebanks (38 languages) in the Universal Dependencies corpus achieve a high accuracy for all languages. Among them, our experiments on 16 treebanks of 12 non-European languages achieve an average UAS absolute improvement of 3.3% over a state-of-the-art method.
Alterations in fear learning/generalization are considered to be relevant mechanisms engendering the development of anxiety disorders being the most prevalent mental disorders. Although anxiety disorders almost exclusively have their first onset in childhood and adolescence, etiological research focuses on adult individuals. In this study, we evaluated findings of a recent meta-analysis of genome-wide association studies in adult anxiety disorders with significant associations of four single nucleotide polymorphisms (SNPs) in a large cohort of 347 healthy children (8-12 years) characterized for dimensional anxiety. We investigated the modulation of anxiety parameters by these SNPs in a discriminative fear conditioning and generalization paradigm in the to-date largest sample of children. Results extended findings of the meta-analysis showing a genomic locus on 2p21 to modulate anxious personality traits and arousal ratings. These SNPs might, thus, serve as susceptibility markers for a shared risk across pathological anxiety, presumably mediated by alterations in arousal.
Automatic emotion regulation (AER) is an important type of emotion regulation in our daily life. Most of the previous studies concerning AER are done in the conscious level. Little is known about the AER under the subliminal level. The present study was to investigate the AER at the different perceptual levels (i.e., explicitly and implicitly) simultaneously, and the associated neural differences using functional magnetic resonance imaging. Priming paradigm was adopted in which the inhibition or neutral words were used as primes and the negative picutres were used as targets. In the experiment, the duration time of priming words was manipulated at 33 or 50 ms in the implicit level and 3000 ms in the explicit level. The participants were required to make emotional valence rating of the negative pictures while undergoing functional magnetic resonance imaging scanning. The results showed that the participants experienced less negative emotion in inhibition words priming condition contrary to neutral words priming condition. Significant differences were also found in the left ventrolateral prefrontal cortex and left dorsolateral prefrontal cortex at the implicit and explicit AER. The findings of this study demonstrate that inhibition words can automatically and effectively reduce an individual's negative emotion experience, and left ventrolateral prefrontal cortex and left dorsolateral prefrontal cortex have been both implicated in self-control during AER.
Mirror-sensory synaesthetes mirror the pain or touch that they observe in other people on their own bodies. This type of synaesthesia has been associated with enhanced empathy. We investigated whether the enhanced empathy of people with mirror-sensory synesthesia influences the experience of situations involving touch or pain and whether it affects their prosocial decision making. Mirror-sensory synaesthetes ( N = 18, all female), verified with a touch-interference paradigm, were compared with a similar number of age-matched control individuals (all female). Participants viewed arousing images depicting pain or touch; we recorded subjective valence and arousal ratings, and physiological responses, hypothesizing more extreme reactions in synaesthetes. The subjective impact of positive and negative images was stronger in synaesthetes than in control participants; the stronger the reported synaesthesia, the more extreme the picture ratings. However, there was no evidence for differential physiological or hormonal responses to arousing pictures. Prosocial decision making was assessed with an economic game assessing altruism, in which participants had to divide money between themselves and a second player. Mirror-sensory synaesthetes donated more money than non-synaesthetes, showing enhanced prosocial behaviour, and also scored higher on the Interpersonal Reactivity Index as a measure of empathy. Our study demonstrates the subjective impact of mirror-sensory synaesthesia and its stimulating influence on prosocial behaviour. This article is part of the discussion meeting issue ‘Bridging senses: novel insights from synaesthesia’.
Characterizing the distribution of crossing dependencies in natural language dependency trees is a crucial task for building parsers and understanding the formal properties of human language. A number of formal restrictions on crossing dependencies have been proposed, including bounds on gap degree, edge degree, and end-point crossings. Here we ask whether the empirical distribution of crossing dependencies in dependency treebanks offers evidence for these formal restrictions as true, independent constraints on dependency trees, or whether the distribution can be explained using other, more generic constraints affecting dependency trees. Specifically, we explore the null hypothesis that crossing dependencies are formally unrestricted, but occur at a low rate. We implement the null hypothesis using random trees where crossing dependencies occur at the same rate as in natural language trees, but without any formal restrictions. We find that this baseline generally does not reproduce the same distribution of gap degree, edge degree, endpoint-crossing, and heads' depth difference as real trees, suggesting that these formal constraints are a consequence of factors beyond the rate of crossing dependencies alone.
The paper studies the effect of emotional states modulated by auditory stimuli on the cognitive control on decision making. Based on other previous neuroimaging studies, functional near-infrared spectroscopy provided reliable neuroimaging measurement in analyzing emotional states by studying the changes of hemodynamic response in prefrontal cortex (PFC). This experiment involved 16 nursing students. During the experiment, participants were given one minute to complete five nursing practice questions with five sequential repetitions in the presence of neutral and negative emotional auditory stimuli in two separated sessions under fNIRS measurement. The sound stimuli was selected from the International Affective Digitized Sound (IADS) System. The neutral auditory stimuli had neutral valence and medium arousal rating whereas negative auditory stimuli had negative valence and high arousal rating. The data collected was preprocessed by using wavelet transform to decompose the data into different frequency intervals. By selecting the frequency interval of interest, we analyzed the data based on functional connectivity within prefrontal cortex regions. We computed the regional wavelet coherence values between affective and neutral tasks. From the behavioral analysis, we found that subjects had significantly higher accuracy in affective task compared to neutral task. Based on the analysis, we found that left prefrontal cortex produced significantly lower wavelet coherence value but the highest coherence-accuracy correlation in affective task than in neutral task.
The Penn Treebank (PTB) represents syntactic structures as graphs due to nonlocal dependencies. This paper proposes a method that approximates PTB graph-structured representations by trees. By our approximation method, we can reduce nonlocal dependency identification and constituency parsing into single treebased parsing. An experimental result demonstrates that our approximation method with an off-the-shelf tree-based constituency parser significantly outperforms the previous methods in nonlocal dependency identification.
Many advances in Natural Language Processing have been based upon more expressive models for how inputs interact with the context in which they occur. Recurrent networks, which have enjoyed a modicum of success, still lack the generalization and systematicity ultimately required for modelling language. In this work, we propose an extension to the venerable Long Short-Term Memory in the form of mutual gating of the current input and the previous output. This mechanism affords the modelling of a richer space of interactions between inputs and their context. Equivalently, our model can be viewed as making the transition function given by the LSTM context-dependent. Experiments demonstrate markedly improved generalization on language modelling in the range of 3-4 perplexity points on Penn Treebank and Wikitext-2, and 0.01-0.05 bpc on four character-based datasets. We establish a new state of the art on all datasets with the exception of Enwik8, where we close a large gap between the LSTM and Transformer models.
70% of patients with schizophrenia suffer from auditory verbal hallucinations (AVH) which are frequently described as distressing and disabling. The content of AVH, in relation to internal thought, has never been linguistically tested in a self-monitoring study. The aim of this preliminary study was to establish if there was a significant difference between AVH and inner thoughts on the key linguistic parameters of valence (pleasantness), dominance (control) and arousal (intensity of emotion produced). Six volunteers with a diagnosis of schizophrenia from voice hearing support groups produced real-time, detailed diaries of AVH and inner thoughts using randomised/fixed timers. Analysis of content was completed using an established linguistic database. AVH were significantly more unpleasant and controlling but not more emotionally arousing than inner thoughts. Psychoeducation around the experience of hallucination in schizophrenia should include information that the voices will be significantly more unpleasant and controlling than their own thoughts but not more emotionally arousing. CBT might therefore include the use of compassion focussed techniques to help with the unpleasantness of AVH and schema level techniques to improve coping with the dominance of AVH.
Classifying patients' affect is a pivotal part of the mental status examination. However, this common practice is often widely inconsistent between raters. Recent advances in the field of Facial Action Recognition (FAR) have enabled the development of tools that can act to identify facial expressions from videos. In this study, we aimed to explore the potential of using machine learning techniques on FAR features extracted from videotaped semi-structured psychiatric interviews of 25 male schizophrenia inpatients (mean age 41.2 years, STD = 11.4). Five senior psychiatrists rated patients' affect based on the videos. Then, a novel computer vision algorithm and a machine learning method were used to predict affect classification based on each psychiatrist affect rating. The algorithm is shown to have a significant predictive power for each of the human raters. We also found that the eyes facial area contributed the most to the psychiatrists' evaluation of the patients' affect. This study serves as a proof-of-concept for the potential of using the machine learning FAR system as a clinician-supporting tool, in an attempt to improve the consistency and reliability of mental status examination.
Abstract This chapter poses the question of whether humans might be essentially normative animals, i.e. whether traditionally prominent specificities of the human life form—our linguistic, social, and moral “natures”—might ground in a basic susceptibility, or proclivity to the deontic regulation of thought and behaviour: the “normative animal thesis.” The chapter lays out the issues at stake in attempting to answer this question. It divides into two main parts. The first begins by clarifying the—norm-related—concept of normativity at issue, distinguishing it from the—reason-related—conceptualisation current in meta-ethics and theories of rationality. It then discusses the primary candidates for generic features of norms, before dividing the normative animal thesis into various sub-claims. The second part presents the key questions at issue in the discussion of social, moral, and linguistic norms, comparing ways of conceiving them and marking the significance of such conceptualisations for the normative animal thesis.
Purpose Verbs with low concreteness are frequent in discourse samples but rarely targeted in aphasia treatments for verbs. These verbs are an important part of functional communication, and recent studies have called for more research regarding aphasia and treatment stimuli with low concreteness. The aim of this study was to pilot the use of verbs with low concreteness in a novel sentence production intervention with persons with aphasia. Method The study took the form of a single-case experimental design with multiple baselines across behaviors and across participants. Three persons with chronic nonfluent aphasia and apraxia of speech participated in the study. Each participant received treatment designed to increase the semantic networks of verbs with high frequency and low concreteness. Sentence production was closely examined over the course of treatment for treated and untreated verbs of varying concreteness levels. Additional measures of language and cognitive functioning were also taken before and after treatment. Results Results indicated improved sentence production with target verbs attributable to the treatment in the 1st phase of 2 phases for 2 of the 3 participants. The increases corresponded with the application of treatment, despite the difference in number of baseline sessions for the participants. Where there were treatment effects, there was also considerable generalization to untreated sets of items during the 1st treatment phase. Word retrieval also improved for 2 participants. Conclusions The results suggest that the novel treatment may improve sentence production and word retrieval in persons with aphasia, even when using target verbs with low concreteness ratings. Future research is warranted into the use of low concreteness verbs. Supplemental Material https://doi.org/10.23641/asha.10870958.
Drawing on linguistic ethnographic data analysis, this article aims to expand the Rampton’s concept of ‘language crossing’ through integrating the notion of ‘linguistic racism’ experienced by Mongolian background immigrant women in Australia. These women encounter linguistic homogeneity, discrimination, and alienation in varied ways in their daily institutional and non-institutional settings based on how they speak English or their usage of heritage languages. As a result, they establish everyday linguistic resistance strategies to combat linguistic racism, which further add two new dimensions to the concept of language crossing – ‘crossing as a resistance strategy’ and ‘crossing as a passing strategy’. Adopting these crossing strategies allow these women to use their preferred forms of communication to resist dominant linguistic norms and standards in the dominant culture. These strategies further make it possible for these speakers to pass as the native speakers of that dominant language. Finally, the paper argues that it is almost impossible to understand ‘language crossing’ as a discrete understanding isolated from the concept of ‘linguistic racism’. It is better to examine these concepts together, as they seem to complement each other in terms of investigating the everyday linguistic practices, sociolinguistic realities and struggles that these immigrant women encounter.
This paper presents a novel crowd-sourced resource for multimodal discourse: our resource characterizes inferences in image-text contexts in the domain of cooking recipes in the form of coherence relations. Like previous corpora annotating discourse structure between text arguments, such as the Penn Discourse Treebank, our new corpus aids in establishing a better understanding of natural communication and common-sense reasoning, while our findings have implications for a wide range of applications, such as understanding and generation of multimodal documents.
We present a novel semantic framework for modeling linguistic expressions of generalization— generic, habitual, and episodic statements—as combinations of simple, real-valued referential properties of predicates and their arguments. We use this framework to construct a dataset covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to probe the efficacy of type-level and token-level information—including hand-engineered features and static (GloVe) and contextual (ELMo) word embeddings—for predicting expressions of generalization.
Highly regularized LSTMs achieve impressive results on several benchmark datasets in language modeling. We propose a new regularization method based on decoding the last token in the context using the predicted distribution of the next token. This biases the model towards retaining more contextual information, in turn improving its ability to predict the next token. With negligible overhead in the number of parameters and training time, our Past Decode Regularization (PDR) method improves perplexity on the Penn Treebank dataset by up to 1.8 points and by up to 2.3 points on the WikiText-2 dataset, over strong regularized baselines using a single softmax. With a mixture-of-softmax model, we show gains of up to 1.0 perplexity points on these datasets. In addition, our method achieves 1.169 bits-per-character on the Penn Treebank Character dataset for character level language modeling. Each of these results constitute improvements over models without PDR in their respective settings.
Dependency grammar induction is the task of learning dependency syntax without annotated training data. Traditional graph-based models with global inference achieve state-ofthe-art results on this task but they require O(n3) run time. Transition-based models enable faster inference with O(n) time complexity, but their performance still lags behind. In this work, we propose a neural transition-based parser for dependency grammar induction, whose inference procedure utilizes rich neural features with O(n) time complexity. We train the parser with an integration of variational inference, posterior regularization and variance reduction techniques. The resulting framework outperforms previous unsupervised transition-based dependency parsers and achieves performance comparable to graph-based models, both on the English Penn Treebank and on the Universal Dependency Treebank. In an empirical comparison, we show that our approach substantially increases parsing speed over graphbased models.
The goal of this paper is to use all available Polish language data sets to seek the best possible performance in supervised sentiment analysis of short texts. We use text collections with labeled sentiment such as tweets, movie reviews and a sentiment treebank, in three comparison modes. In the first, we examine the performance of models trained and tested on the same text collection using standard cross-validation (in-domain). In the second we train models on all available data except the given test collection, which we use for testing (one vs rest cross-domain). In the third, we train a model on one data set and apply it to another one (one vs one cross-domain). We compare wide range of methods including machine learning on bag-of-words representation, bidirectional recurrent neural networks as well as the most recent pre-trained architectures ELMO and BERT. We formulate conclusions as to cross-domain and in-domain performance of each method. Unsurprisingly, BERT turned out to be a strong performer, especially in the cross-domain setting. What is surprising however, is solid performance of the relatively simple multinomial Naive Bayes classifier, which performed equally well as BERT on several data sets.
Music is hierarchically structured, both in how it is perceived by listeners and how it is composed. Such structure can be captured elegantly using probabilistic grammatical models similar to those used to study natural language. They address the complexity of the structure using abstract categories in a recursive formalism. Most existing grammatical models of musical structure focus on one single dimension of music--such as melody, harmony, or rhythm. While these grammar models often work well on short musical excerpts, accurate analysis of longer pieces requires taking into account the constraints from multiple domains of structure. The present paper proposes abstract product grammars--a formalism which integrates multiple dimensions of musical structure into a single grammatical model--along with efficient parsing and inference algorithms for this formalism. We use this model to study the combination of hierarchically-structured harmonic syntax and hierarchically-structured rhythmic information. The latter is modeled by a novel grammar of rhythm that is capable of expressing temporal regularities in musical phrases. It integrates grouping structure and meter. The combined model of harmony and rhythm outperforms both single-dimension models in computational experiments. All models are trained and evaluated on a treebank of hand-annotated Jazz standards.
Music has been shown to influence the behavioral responses of individuals in real-world scenarios, but little research exists on the effects that music has on the in-game behaviors of video game players. A song can be rated in terms of the level of arousal, or emotional intensity, it incites, and this study explores how music of various arousal ratings can be used to influence players' choices in an interactive narrative role-playing game. We hypothesized that high-arousal music would influence players to exhibit avoidance behaviors in-game, and that low-arousal music would influence players to exhibit social behaviors. Experimentation showed that players were statistically significantly more likely to make avoidance behavior choices when high-arousal music was played. These findings are the first step into understanding how music can be used by game developers to influence player behaviors in interactive narrative games.
Although SGD requires shuffling the training data between epochs, currently none of the word-level language modeling systems do this. Naively shuffling all sentences in the training data would not permit the model to learn inter-sentence dependencies. Here we present a method that partially shuffles the training data between epochs. This method makes each batch random, while keeping most sentence ordering intact. It achieves new state of the art results on word-level language modeling on both the Penn Treebank and WikiText-2 datasets.
The Ministry of Education and Science of the Russian Federation has established a score-rating system to assess the achievement quality within academic disciplines. The word "rating" is a foreign-language term, literal translation into Russian means "assessment". The second meaning of the word "rating" is a numerical measure; it characterizes performance of a student, pupil, etc., during a certain period of training, usually by the 20-point scale, or 100-point scale. This article highlights the history of formation and implementation of a score-rating system into the curriculum. Due to development of advanced technologies, there is a need for training highly qualified personnel to solve increasingly complex problems in professional activities. In our opinion, pedagogical assessment of student's knowledge in the form of score-rating system is a key role for preparing highly qualified personnel. The authors study processes of training skilled professionals in the course of academic education in technical universities of our country. Key words: score-rating system (SRS), competence, student, progress, credit, rating, factor.
This article contends that researchers can and should be active participants in making sound archives more accessible. In fact, such advocacy can be consequential in setting up possibilities for creative research on race within radio history and sound studies. Using the example of NPR’s All Things Considered archive spanning from 1971–1983, I demonstrate how academics and archivists can work together to make possible the preservation and accessibility of sound archives. This particular collaboration offers an opportunity to take a cultural approach to understanding newsroom diversity, more specifically: the cultural constraints of linguistic norms and the emergent cultures that arise as aberrations from such norms. The article reflects on this project’s implications for other scholars who work with archives that wish to invest in sound archive preservation and use.
The slowness of legal proceedings in the common law legal system is a widely known fact. Any tool which could help reduce the time taken for the resolution of a case is invaluable. Common legal systems place a great importance on precedents and retrieving the correct set of precedents is considerably time consuming. Hence, for any case whose proceedings are in progress, if there are suitable prior cases, then the court has to follow the same interpretations that were passed in the prior cases. This is to ensure that similar situations receive similar treatment, thus maintaining uniformity amongst the legal proceedings across all courts at all times. Hence, precedent cases are treated as important as any other written law (a statute) in this legal system. In this paper, we propose two new approaches to solve this information retrieval problem wherein the system accepts the current case document as the query and returns the relevant precedent cases as the result. The first approach is to calculate the document similarity using Wordnet, which is a lexical database that could be leveraged to quantify the semantic relatedness between two documents, using a semantic network. The second approach is the use of a Siamese Manhattan Long Short Term Memory network, which is a supervised model trained to understand the underlying similarity between two documents.
The paper presents a short introduction to several electronic resources for Ukrainian language, namely, two treebanks: the Gold standard (ab. 130 thousand tokens), manually annotated in the Universal Dependencies flavour (https://universaldependencies.org/), which comprises the training data for a machine-trained syntactic parser, and a big (near 3 billion tokens),
Dialectology and Dutch syntax from the perspective of linguistic (norm) change Jeroen van Craenenbroeck demonstrates in a highly convincing way that both synchronous descriptions and theoretical approaches of the syntax of Standard Dutch can be optimized by including analyses of dialect variation. Besides a minor reservation with respect to van Craenenbroeck’s interpretation of the conjugation of conjunctions, this response adds a complementary perspective to van Craenenbroeck’s overall argumentation by arguing that the inclusion of geolinguistic research on dialect variation is also indispensable for the diachronic study of Dutch syntax.