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16504 papers
We introduce three techniques for improving constituent parsing for morphologically rich languages. We propose a novel approach to automatically find an optimal preterminal set by clustering morphological feature values and we conduct experiments with enhanced lexical models and feature engineering for rerankers. These techniques are specially designed for morphologically rich languages (but they are language-agnostic). We report empirical results on the treebanks of five morphologically rich languages and show a considerable improvement in accuracy and in parsing speed as well.
There are all too few examples of good urban governance in the ‘South’. One city which improved its performance dramatically after 1992 was Bogotá, the capital of Colombia. It joined the ranks of exemplar cities and its former mayors toured the world advertising this ‘miracle’. Unfortunately, after 2008, the city’s administration became mired in corruption and its image ratings have dived. The current administration has so far failed to revive trust in the city’s governance. Based on interviews with key personalities in the city, this paper examines the causes of Bogotá’s recovery and its recent relapse. Bogotá’s experience is useful to students of urban governance in showing not only how a city in the ‘South’ can improve its performance but also that any improvement is fragile. A decent working relationship between technocrats and politicians is critical in guaranteeing both public support and progress in implementing major public works.
OBJECTIVE: To investigate the perception of facial asymmetry in young adults to identify the amounts of chin asymmetry that can be regarded as normal and may benefit from correction. MATERIALS AND METHODS: Three-dimensional (3D) images of 56 individuals of mixed ethnicity were obtained and used to produce average 3D images of male and female faces. Distortion was then applied to these average faces using a 3D graphics package to simulate different amounts of chin point asymmetry. Five observer groups (lay individuals, dental students, dental care professionals, dental practitioners, and orthodontists) assessed timed presentations of 3D images, rating them as "normal," "acceptable," or "would benefit from correction." Time-to-event analysis was used to assess the level of chin asymmetry perceived as normal and beneficial for correction for each group. RESULTS: The factors influencing the perception of facial asymmetry were the degree of asymmetry and the observer group. Direction of the asymmetry and gender of the assessed individual did not affect the perception of asymmetry, except in the 4- to 6-mm distortion range. The gender of the observer had no influence on perception. There were statistically significant differences in the amounts of asymmetry that the laypeople and orthodontists considered to be normal (5.6 ± 2.7 mm and 3.6 ± 1.5 mm, respectively; P <.001) and felt would benefit from surgical correction (11.8 ± 4.0 mm and 9.7 ± 3.0 mm, respectively; P =.001). CONCLUSIONS: Perception of asymmetry is affected by the amount of asymmetry and the observer group, with orthodontists being more critical.
This paper presents several techniques for managing ambiguity in LFG parsing of Wolof, a less-resourced Niger-Congo language. Ambiguity is pervasive in Wolof and This raises a number of theoretical and practical issues for managing ambiguity associated with different objectives. From a theoretical perspective, the main aim is to design a large-scale grammar for Wolof that is able to make linguistically motivated disambiguation decisions, and to find appropriate ways of controlling ambiguity at important interface representations. The practical aim is to develop disambiguation strategies to improve the performance of the grammar in terms of efficiency, robustness and coverage.To achieve these goals, different avenues are explored to manage ambiguity in the Wolof grammar, including the formal encoding of noun class indeterminacy, lexical specifications, the use of Constraint Grammar models (Karlsson 1990) for morphological disambiguation, the application of the c-structure pruning mechanism (Cahill et al. 2007, 2008; Crouch et al. 2013), and the use of optimality marks for preferences (Frank et al. 1998, 2001). The parsing system is further controlled by packing ambiguities. In addition, discriminant-based techniques for parse disambiguation (Rosén et al. 2007) are applied for treebanking purposes.
Sentence similarity measures play an increasingly important role in text-related research and applications in areas such as text mining, Web page retrieval, and dialogue systems. Existing methods for computing sentence similarity have been adopted from approaches used for long text documents. These methods process sentences in a very high-dimensional space and are consequently inefficient, require human input, and are not adaptable to some application domains. This paper focuses directly on computing the similarity between very short texts of sentence length. It presents an algorithm that takes account of semantic information, structure information and word order information implied in the sentences. The semantic similarity of two sentences is calculated using information from a structured lexical database, How-net. The use of a lexical database enables our method to model human common sense knowledge. The proposed method can be used in a variety of applications that involve text knowledge representation and discovery. Experiments on two sets of selected sentence pairs demonstrate that the proposed method provides a similarity measure that shows higher accuracy than other methods.
OBJECTIVE: To develop and evaluate an objective image-scoring system for crown-rump length (CRL) measurements and to determine how this compares with subjective assessment. METHODS: A total of 125 CRL ultrasound images were selected from the database of the International Fetal and Newborn Growth Consortium for the 21(st) Century study group. Two reviewers, who were blinded to the operators' and to each others' results, evaluated all images both subjectively and objectively. Subjective evaluation consisted of rating an image as acceptable or unacceptable, while objective evaluation was based on six criteria. Reviewer differences for both the subjective and objective evaluations were compared using percentage of agreement and adjusted kappa values. RESULTS: The distribution of individual scores and differences between subjective and objective evaluation for the two reviewers was similar. Overall agreement between the reviewers was higher for objective evaluation (95.2%; adjusted κ, 0.904), than for subjective evaluation (77.6%; adjusted κ, 0.552). There was a high level of agreement for horizontal position (κ = 0.951), magnification (κ = 0.919), visualization of crown and rump (κ = 0.806) and caliper placement (κ = 0.756), while agreement for mid-sagittal section (κ = 0.629) and neutral position (κ = 0.565) were moderate and poor, respectively. CONCLUSION: The proposed six-point scoring system for CRL image rating is more reproducible than is subjective evaluation and should be considered as a method of quality assessment and audit.
In this paper we present the principles of lexico-semantic annotation of Skadnica Treebank using Polish WordNet lexical units.We describe different means of annotation, depending on the structure of a sentence in Skadnica on the one hand and the availability of adequate lexical unit in PLWN on the other.Apart from "standard" annotation involving lexical units with the same lemma as the token under annotation, multi-word units, different verb lemmas including reflexive marker si as well as synonyms and hypernyms have also been involved.Some tokens have obtained tags explaining why they require no annotation.Additionally, we discuss the assessment of the annotation of whole sentences.
We propose a transition system for dependency parsing with a left-corner parsing strategy. Unlike parsers with conventional transition systems, such as arc-standard or arc-eager, a parser with our system correctly predicts the processing difficulties people have, such as of center-embedding. We characterize our transition system by comparing its oracle behaviors with those of other transition systems on treebanks of 18 typologically diverse languages. A crosslinguistical analysis confirms the universality of the claim that a parser with our system requires less memory for parsing naturally occurring sentences.
Hybrid approaches for automatic vowelization of Arabic texts are presented in this article. The process is made up of two modules. In the first one, a morphological analysis of the text words is performed using the open source morphological Analyzer AlKhalil Morpho Sys. Outputs for each word analyzed out of context, are its different possible vowelizations. The integration of this Analyzer in our vowelization system required the addition of a lexical database containing the most frequent words in Arabic language. Using a statistical approach based on two hidden Markov models (HMM), the second module aims to eliminate the ambiguities. Indeed, for the first HMM, the unvowelized Arabic words are the observed states and the vowelized words are the hidden states. The observed states of the second HMM are identical to those of the first, but the hidden states are the lists of possible diacritics of the word without its Arabic letters. Our system uses Viterbi algorithm to select the optimal path among the solutions proposed by Al Khalil Morpho Sys. Our approach opens an important way to improve the performance of automatic vowelization of Arabic texts for other uses in automatic natural language processing.
Researchers from various disciplines are concerned with the study of affective phenomena, especially arousal. Expressed affective modulations, which reflect both an individual’s in-ternal state and external factors, are central to the commu-nicative process. Bone et al. developed a robust, unsuper-vised (rule-based) method which provides a scale-continuous, bounded arousal rating from the vocal signal. In this study, we investigate the joint-dynamics of child and psychologist vocal arousal in autism spectrum disorder (ASD) diagnostic interac-tions. Arousal synchrony is assessed with multiple methods. Results indicate that children with higher ASD severity tend to lead the arousal dynamics more, seemingly because the children aren’t as responsive to the psychologist’s affective modulations. A vocal arousal model is also proposed which incorporates so-cial and conversational constructs. The model captures conver-sational signal relations, and is able to distinguish between high and low ASD severity at accuracies well-above chance.
Transition-based dependency parsing systems can utilize rich feature representations. However, in practice, features are generally limited to combinations of lexical tokens and part-of-speech tags. In this paper, we investigate richer features based on supertags, which represent lexical templates extracted from dependency structure annotated corpus. First, we develop two types of supertags that encode information about head position and dependency relations in different levels of granularity. Then, we propose a transition-based dependency parser that incorporates the predictions from a CRF-based supertagger as new features. On standard English Penn Treebank corpus, we show that our supertag features achieve parsing improvements of 1.3% in unlabeled attachment, 2.07% root attachment, and 3.94% in complete tree accuracy.
We propose in this paper a supervised learning approach to identify discourse relations in Arabic texts. To our knowledge, this work represents the first attempt to focus on both explicit and implicit relations that link adjacent as well as non adjacent Elementary Discourse Units (EDUs) within the Segmented Discourse Representation Theory (SDRT). We use the Discourse Arabic Treebank corpus (D-ATB) which is composed of newspaper documents extracted from the syntactically annotated Arabic Treebank v3.2 part3 where each document is associated with complete discourse graph according to the cognitive principles of SDRT. Our list of discourse relations is composed of a three-level hierarchy of 24 relations grouped into 4 top-level classes. To automatically learn them, we use state of the art features whose efficiency has been empirically proved. We investigate how each feature contributes to the learning process. We report our experiments on identifying fine-grained discourse relations, mid-level classes and also top-level classes. We compare our approach with three baselines that are based on the most frequent relation, discourse connectives and the features used by Al-Saif and Markert (2011). Our results are very encouraging and outperform all the baselines with an F-score of 78.1% and an accuracy of 80.6%.
OBJECTIVE: Emotional reactivity in bipolar affective disorders has received increased attention as a relevant issue with regard to the ability to respond to emotional external stimuli for individual real world adaptation. We investigated emotional reactivity using the International Affective Picture System (IAPS) paradigm in bipolar patients during the depressive phase compared to healthy controls. METHOD: Twenty-three bipolar patients with a major depressive episode without manic symptoms and 27 healthy control subjects were recruited. They were asked to judge their emotional reactivity while viewing 90 pictures selected from the IAPS. Their ratings were categorized according to the emotional valence and arousal in response to pleasant, neutral, and unpleasant stimuli. RESULTS: The patients showed lower valence ratings for neutral pictures compared to healthy subjects. No significant between-group differences were found for the pleasant and unpleasant pictures. Higher activation for patients to all emotional stimuli was seen. CONCLUSION: Patients during the depressive phase gave more negative valence to neutral images. This can suggest that they are more pessimistic in the way they perceive the environment as more reactive to emotional cues.
While smokers are known to find smoking-related stimuli motivationally salient, the extent to which former smokers do so is largely unknown. In this study, we collected event-related potential (ERP) data from former and never smokers and compared them to a sample of current smokers interested in quitting who completed the same ERP paradigm prior to smoking cessation treatment. All participants (n = 180) attended 1 laboratory session where we recorded dense-array ERPs in response to cigarette-related, pleasant, unpleasant, and neutral pictures and where we collected valence and arousal ratings of the pictures. We identified 3 spatial and temporal regions of interest, corresponding to the P1 (120-132 ms), early posterior negativity (EPN; 244-316 ms), and late positive potential (LPP; 384-800 ms) ERP components. We found that all participants produced larger P1 responses to cigarette-related pictures compared to the other picture categories. With the EPN component, we found that, similar to pleasant and unpleasant pictures, cigarette-related pictures attracted early attentional resources, regardless of smoking status. Both former and never smokers produced reduced LPP responses to cigarette-related and pleasant pictures compared to current smokers. Current smokers rated the cigarette-related pictures as being more pleasant and arousing than the former and never smokers. The LPP and picture-rating results suggest that former smokers, like never smokers, do not find cigarette-related stimuli to be as motivationally salient as current smokers.
status: Published
Less-configurational languages such as German often show not just morphological variation but also free word order and nonprojectivity. German is not exceptional in this regard, as other morphologically-rich languages such as Czech, Tamil or Greek, offer similar challenges that make context-free constituent parsing less attractive. Advocates of dependency parsing have long pointed out that the free(r) word order and non-projective phenomena are handled in a more straightforward way by dependency parsing. How-ever, certain other phenomena in language, such as gapping, ellipses or verbless sentences, are difficult to handle in a dependency formalism. In this paper, we show that parsing of discontinuous constituents can be achieved using easy-first parsing with online reordering, an approach that previously has only been used for dependencies, and that the approach yields very fast parsing with reasonably accurate results that are close to the state of the art, surpassing existing results that use treebank grammars. We also investigate the question whether phenomena where dependency representations may be problematic – in particular, verbless clauses – can be handled by this model. 1
Dependency parsing is a core task in NLP, and it is widely used by many applications such as information extraction, question answering, and machine translation. In the era of social media, a big challenge is that parsers trained on traditional newswire corpora typically suffer from the domain mismatch issue, and thus perform poorly on social media data. We present a new GFL/FUDG-annotated Chinese treebank with more than 18K tokens from Sina Weibo (the Chinese equivalent of Twitter). We formulate the dependency parsing problem as many small and parallelizable arc prediction tasks: for each task, we use a programmable probabilistic firstorder logic to infer the dependency arc of a token in the sentence. In experiments, we show that the proposed model outperforms an off-the-shelf Stanford Chinese parser, as well as a strong MaltParser baseline that is trained on the same in-domain data.
Discourse parsing is a challenging task and plays a critical role in discourse analysis. In this paper, we focus on labeling full argument spans of discourse connectives in the Penn Discourse Treebank (PDTB). Previous studies cast this task as a linear tagging or subtree extraction problem. In this paper, we propose a novel constituent-based approach to argument labeling, which integrates the advantages of both linear tagging and subtree extraction. In particular, the proposed approach unifies intra-and intersentence cases by treating the immediately preceding sentence as a special constituent. Besides, a joint inference mechanism is introduced to incorporate global information across arguments into our constituent-based approach via integer linear programming. Evaluation on PDT-B shows significant performance improvements of our constituent-based approach over the best state-of-the-art system. It also shows the effectiveness of our joint inference mechanism in modeling global information across arguments.
We present novel computational experiments using William Labov’s theory of narrative analysis. We describe his six elements of narrative structure and construct a new corpus based on his most recent work on narrative. Using this corpus, we explore the correspondence between Labovs elements of narrative structure and the implicit discourse relations of the Penn Discourse Treebank, and we construct a mapping between the elements of narrative structure and the discourse relation classes of the PDTB. We present first experiments on detecting Complicating Actions, the most common of the elements of narrative structure, achieving an f-score of 71.55. We compare the contributions of features derived from narrative analysis, such as the length of clauses and the tenses of main verbs, with those of features drawn from work on detecting implicit discourse relations. Finally, we suggest directions for future research on narrative structure, such as applications in assessing text quality and in narrative generation.
The increasing diversity of languages used on the web introduces a new level of complexity to Information Retrieval (IR) systems. We can no longer assume that textual content is written in one language or even the same language family. In this paper, we demonstrate how to build massive multilingual annotators with minimal human expertise and intervention. We describe a system that builds Named Entity Recognition (NER) annotators for 40 major languages using Wikipedia and Freebase. Our approach does not require NER human annotated datasets or language specific resources like treebanks, parallel corpora, and orthographic rules. The novelty of approach lies therein - using only language agnostic techniques, while achieving competitive performance. Our method learns distributed word representations (word embeddings) which encode semantic and syntactic features of words in each language. Then, we automatically generate datasets from Wikipedia link structure and Freebase attributes. Finally, we apply two preprocessing stages (oversampling and exact surface form matching) which do not require any linguistic expertise. Our evaluation is two fold: First, we demonstrate the system performance on human annotated datasets. Second, for languages where no gold-standard benchmarks are available, we propose a new method, distant evaluation, based on statistical machine translation.
Processing unpleasant affective cues induces elevated momentary symptom reports, especially in persons with high levels of symptom reporting in daily life. The present study aimed to examine whether applying an emotion regulation strategy, i.e. affect labeling, can inhibit these emotion influences on symptom reporting. Student participants (N = 61) with varying levels of habitual symptom reporting completed six picture viewing trials of homogeneous valence (three pleasant, three unpleasant) under three conditions: merely viewing, emotional labeling, or content (non-emotional) labeling. Affect ratings and symptom reports were collected after each trial. Participants completed a motor inhibition task and self-control questionnaires as indices of their inhibitory capacities. Heart rate variability was also measured. Labeling, either emotional or non-emotional, significantly reduced experienced affect, as well as the elevated symptoms reports observed after unpleasant picture viewing. These labeling effects became more pronounced with increasing levels of habitual symptom reporting, suggesting a moderating role of the latter variable, but did not correlate with any index of general inhibitory capacity. Our findings suggest that using an emotion regulation strategy, such as labeling emotional stimuli, can reverse the effects of unpleasant stimuli on symptom reporting and that such strategies can be especially beneficial for individuals suffering from medically unexplained physical symptoms.
A phrase dependency treebank(PDT)integrating phrase structure grammar and dependency grammar is proposed and elaborated to cater for translation studies.The construction of DUT Parallel Chinese-English PDT(DUT-CEPDT)is reported.PDT favors flat structures and the dependency is based on semantics rather than syntactic functions,which differs from the mainstream dependency analysis that favors binary branching.The raw texts of DUT-CEPDT are Chinese government work reports and White Papers and their official English translation.First of all,after word segmentation and part of speech(POS)tagging,Chinese PDT and English PDT are constructed manually with the aid of LingTreeConstructor,a tool tailored for linguists.Then,node alignment, which covers translation alignments of words,phrases,clauses up to the whole passage,is proposed instead of traditional word or sentence alignment to provide more translation knowledge.Lastly, semantic roles based on the FrameNet are labeled simultaneously on the aligned nodes of the English and Chinese trees.DUT-CEPDT can serve as a resource and standard of the training and assessment of both human translators and machine translation systems.
In this paper, we report the obtained results of two constituency parsers trained with BulTreeBank, an HPSG-based treebank for Bulgarian. To reduce the data sparsity problem, we propose using the Brown word clustering to do an off-line clustering and map the words in the treebank to create a class-based treebank. e observations show that when the classes outnumber the POS tags, the results are beer. Since this approach adds on another dimension of abstraction (in comparison to the lemma), its coarse-grained representation can be used further for training statistical parsers.
This chapter investigates problems that novice writers, especially learners of English, have in acquiring lexical features of written discourse. It describes features of an interactive writing kit (WordPilot) which assists them to access authentic text, then to transfer patterns they explore there to their own writing. Relevant text corpora and specific lexical databases are made available during the writing process through the mediation of this integrated electronic reference, which incorporates several applications, including a concordancer, dictionary and thesaurus. This learning and production device has been implemented for Cantonese-speaking students in Hong Kong for whom English is a Foreign Language, but the general principles have relevance to writers at any point on the continuum of language proficiency. The chapter also illustrates problems in the presentation of lexical patterns to novice writers of limited English proficiency. As a gateway to text, it provides novice writers opportunities to explore language features, and it assists academic gate keepers in initiating learners into discourse.
This study examines the methodology of global foreign accent ratings in studies on L2 speech production. In three experiments, we test how variation in raters, range within speech samples, as well as instructions and procedures affects ratings of accent in predominantly monolingual speakers of German, non-native speakers of German, as well as long-term emigrants from Germany, that is, L1 attriters. The findings show that rater differences do not result in systematic changes in rating patterns. In contrast, range effects and effects of familiarity with accented speech lead to shifts in absolute and relative ratings. Including more strongly foreign-accented samples leads to lower judgements for the entire group of L2 speakers compared to natives. Similarly, lower familiarity with foreign accent results in more variable and more strongly foreign-accented judgements. We discuss the implications for research on L2 pronunciation as well as for the interpretation of nativeness in L2 studies and language testing more generally.
This paper proposes a simple yet effective framework of soft cross-lingual syntax projection to transfer syntactic structures from source language to target language using monolingual treebanks and large-scale bilingual parallel text. Here, soft means that we only project reliable dependencies to compose high-quality target structures. The projected instances are then used as additional training data to improve the performance of supervised parsers. The major issues for this idea are 1) errors from the source-language parser and unsupervised word aligner; 2) intrinsic syntactic non-isomorphism between languages; 3) incomplete parse trees after projection. To handle the first two issues, we propose to use a probabilistic dependency parser trained on the target-language treebank, and prune out unlikely projected dependencies that have low marginal probabilities. To make use of the incomplete projected syntactic structures, we adopt a new learning technique based on ambiguous labelings. For a word that has no head words after projection, we enrich the projected structure with all other words as its candidate heads as long as the newly-added dependency does not cross any projected dependencies. In this way, the syntactic structure of a sentence becomes a parse forest (ambiguous labels) instead of a single parse tree. During training, the objective is to maximize the mixed likelihood of manually labeled instances and projected instances with ambiguous labelings. Experimental results on benchmark data show that our method significantly outperforms a strong baseline supervised parser and previous syntax projection methods. 1
Recent resting-state functional magnetic resonance imaging (fMRI) studies using graph theory metrics have revealed that the functional network of the human brain possesses small-world characteristics and comprises several functional hub regions. However, it is unclear how the affective functional network is organized in the brain during the processing of affective information. In this study, the fMRI data were collected from 25 healthy college students as they viewed a total of 81 positive, neutral, and negative pictures. The results indicated that affective functional networks exhibit weaker small-worldness properties with higher local efficiency, implying that local connections increase during viewing affective pictures. Moreover, positive and negative emotional processing exhibit dissociable functional hubs, emerging mainly in task-positive regions. These functional hubs, which are the centers of information processing, have nodal betweenness centrality values that are at least 1.5 times larger than the average betweenness centrality of the network. Positive affect scores correlated with the betweenness values of the right orbital frontal cortex (OFC) and the right putamen in the positive emotional network; negative affect scores correlated with the betweenness values of the left OFC and the left amygdala in the negative emotional network. The local efficiencies in the left superior and inferior parietal lobe correlated with subsequent arousal ratings of positive and negative pictures, respectively. These observations provide important evidence for the organizational principles of the human brain functional connectome during the processing of affective information.
The chapter argues that language, which rests on the sharing of linguistic norms, honest information, and moral norms, evolved through a co-evolutionary process with a pivotal role for intersubjectivity. Mainstream evolutionary models, based only on individual-level and gene-level selection, are argued to be incapable to account for such sharing of care, values and information, thus implying the need to evoke multi-level selection, including (cultural) group selection. Four of the most influential current theories of the evolution of human-scale sociality, those of Dunbar, Deacon, Tomasello and Hrdy, are compared and evaluated on the basis of their answers to five questions: (1) Why we and not others? (2) How: by what mechanisms? (3) When? (4) In what kind of social settings? (5) What are the implications for ontogeny? The conclusions are that the theories are to a large degree complementary, and that they all assume, explicitly or not, a role for group selection. Hrdy’s theory, focusing on the evolution of alloparenting, is argued to provide the best explanation for the onset of the evolution of human intersubjectivity, and can furthermore offer a Darwinian framework for Tomasello’s theory of shared intentionality. Deacon’s theory deals rather with the evolution of morality and its co-evolution with “symbolic reference”, but these are necessarily antecedent to the primary evolution of human intersubjectivity. Dunbar’s theory on the transition from “musical” vocal-grooming to vocal “gossip” can be seen as providing a partial explanation for evolution of spoken language, most likely with Homo heidelbergensis 0.5 MYA, but presupposes the capacities accounted for by the other models.
Alexithymia refers to difficulties in recognizing one's own emotions, but difficulties have also been found in the recognition of others' emotions, particularly when the task is not easy. Previous research has demonstrated that, in order to understand other peoples' feelings, observers remap the observed emotion onto their own sensory systems. The aim of the present study was to investigate the ability of high and low alexithymic subjects to remap the emotional expressions of others onto their own somatosensory systems using an indirect task. We used the emotional Visual Remapping of Touch (eVRT) paradigm, in which seeing a face being touched improves detection of near-threshold tactile stimulation concurrently delivered to one's own face. In eVRT, subjects performance is influenced by the emotional content of the stimuli, while they were required to distinguish between unilateral or bilateral tactile stimulation on their own cheeks. The results show that tactile perception was enhanced when viewing touch on a fearful face compared with viewing touch on other expressions in low but not in high alexithymic participants. A negative correlation between TAS-20 alexithymia subscale ("difficulty in identify feelings") and the magnitude of the eVRT effect was also found. Conversely, arousal and valence ratings of emotional faces did not vary as a function of the degree of alexithymia. The results provide evidence that alexithymia is associated with difficulties in remapping seen emotions, particularly fear, onto one's own sensory system. This impairment could be due to an inability to modulate somatosensory system activity according to the observed emotional expression.
Annotating linguistic data is often a complex, time consuming and expensive endeavor. Even with strict annotation guidelines, human subjects often deviate in their analyses, each bring different biases, interpretations of the task and levels of consistency. The aim of this paper is to explore a way to find out the inconsistencies in the corpus TreeBank which is used for syntactic analysis through the procedure we study the inconsistencies of verb phrase tagging in the corpus TreeBank. At the same time, we can analyze the inconsistencies of verb phrase tagging which are found in the corpus TreeBank in order that we can find a way to improve the consistency of verb phrase tagging automatically which is effective to improve the quality of corpus.
This paper presents an experiment investigating the influence that a situational context has upon how people affectively interpret Non-Linguistic Utterances made by a social robot. Subjects were presented five video conditions showing the robot making both a positive and negative utterance, the robot being subject to an action (e.g. receiving a kiss, or a slap), and then two videos showing the combination of the action and the robot reacting with both the positive and negative utterances. For each video an affective rating of valence was provided based upon how the subjects thought the robot felt given what had happened in the video. This was repeated for 5 different action scenarios. Results show that the affective interpretation of an action appears to override that of an utterance, regardless of the affective charge of the utterance. Furthermore, it is shown that if the meaning of the action and utterance are aligned, the overall interpretation is amplified. These findings are considered with respect to the practical use of utterances during social HRI.
The purpose of the present study was to examine (a) the teacher-reported prevalence of attention-deficit/hyperactivity disorder (ADHD) symptoms and associated impairment in a nationally representative sample of children and adolescents and (b) the degree to which prevalence varied as a function of student and teacher characteristics. Teacher-reported symptoms of ADHD based on Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) criteria and teacher-rated impairment were used to estimate prevalence using symptoms and impairment either alone or in combination, and to assess predictors of ADHD using a diverse, nationally representative sample (n = 2,140; 1,070 males, 1,070 females; 54.8% White, non-Hispanic) between 5 to 17 years old (M = 11.53; SD = 3.54). The combination of symptom and impairment ratings yielded the prevalence rate most consistent with prior epidemiological findings. Students' age, gender, racial, and special education status were significant predictors of symptom count and level of symptom-related impairment. It is critically important to simultaneously consider symptoms and symptom-related impairment when identifying students with ADHD. Student and teacher characteristics may affect ratings and identification results.
This is the first attempt at characterizing reading difficulty in Hindi using naturally occurring sentences. We created the Potsdam-Allahabad Hindi Eyetracking Corpus by recording eye-movement data from 30 participants at the University of Allahabad, India. The target stimuli were 153 sentences selected from the beta version of the Hindi-Urdu treebank. We find that word- or low-level predictors (syllable length, unigram and bigram frequency) affect first-pass reading times, regression path duration, total reading time, and outgoing saccade length. An increase in syllable length results in longer fixations, and an increase in word unigram and bigram frequency leads to shorter fixations. Longer syllable length and higher frequency lead to longer outgoing saccades. We also find that two predictors of sentence comprehension difficulty, integration and storage cost, have an effect on reading difficulty. Integration cost (Gibson, 2000) was approximated by calculating the distance (in words) between a dependent and head; and storage cost (Gibson, 2000), which measures difficulty of maintaining predictions, was estimated by counting the number of predicted heads at each point in the sentence. We find that integration cost mainly affects outgoing saccade length, and storage cost affects total reading times and outgoing saccade length. Thus, word-level predictors have an effect in both early and late measures of reading time, while predictors of sentence comprehension difficulty tend to affect later measures. This is, to our knowledge, the first demonstration using eye-tracking that both integration and storage cost influence reading difficulty.
Official releases of the PROIEL treebank of ancient Indo-European languages
This paper is a brief review of three current efforts to provide an open and transparent path to the automated production of event data: • EL:DIABLO: an open, user-friendly modular system for the acquisition and coding of web-based news sources which is intended to allow small research teams to generate customized event data sets with a minimum of effort • PETRARCH: a Python-based event data coder using fully-parsed Penn Treebank input • The Open Event Data Alliance, a new professional organization for the promotion and provision of fully transparent open event data All truth passes through three stages. First, it is ridiculed. Second, it is violently opposed. Third, it is accepted as being self-evident. Arthur Schopenhauer