Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
16504 papers
Psycholinguistic research shows that key properties of the human sentence processor are incrementality, connectedness (partial structures contain no unattached nodes), and prediction (upcoming syntactic structure is anticipated). There is currently no broad-coverage parsing model with these properties, however. In this article, we present the first broad-coverage probabilistic parser for PLTAG, a variant of TAG that supports all three requirements. We train our parser on a TAG-transformed version of the Penn Treebank and show that it achieves performance comparable to existing TAG parsers that are incremental but not predictive. We also use our PLTAG model to predict human reading times, demonstrating a better fit on the Dundee eye-tracking corpus than a standard surprisal model.
Grote verzamelingen van vertaalde teksten – zogenaamde parallelle corpora - worden vaak automatisch op zins- en woordniveau gealigneerd om automatische vertaalsystemen op te trainen. Soms voegt men ook automatisch syntactische bomen aan de zinnen toe om meer taalkundige informatie eruit te kunnen halen. Als die bomen aan beide kanten verschijnen en de boomknopen ook worden gealigneerd, is er sprake van een parallelle treebank. De beste vertaalsystemen zijn bijna of helemaal puur statistisch, maar in recente jaren ontstond er een grotere nadruk op de integratie van meer taalkundig gemotiveerde data, waaronder ook het gebruik van parallel treebanks. Ze zijn echter alleen op een zeer grote schaal bruikbaar, omdat er door zo een systeem veel te leren is van hoe een taal typisch naar een andere moet worden omgezet. Daarom onderzoeken we technieken om automatisch de boomknopen accuraat te aligneren. Een bijkomend motief is het feit dat parallel treebanks ook voor andere applicaties bruikbaar zijn en als taalbronnen zelf van wetenschappelijk belang zijn. Het hele proces van het aligneren van knopen noemen wij tree alignment. Wij vinden dat een combinatie van statistiche en regelgebaseerde technieken met relatief weinig trainingsgegevens en weinig features zeer accurate alignments kan produceren. Ten slotte vinden we dat, wanneer wij alignments die relatief heel veel knopen aligneren – al zijn sommigen soms fout – op een syntactisch gebaseerde systeem toepassen, dat tot verbeterde automatische vertaling leidt, in vergelijking met hetzelfde systeem die op minder maar meer accurate alignments getrained is.
Dependency analysis relies on morphosyntactic evidence, as well as semantic evidence. In some cases, however, morphosyntactic evidence seems to be in conflict with semantic evidence. For this reason dependency grammar theories, annotation guidelines and tree-to-dependency conversion schemes often differ in how they analyze various syntactic constructions. Most experiments for which constituent-based treebanks such as the Penn Treebank are converted into dependency treebanks rely blindly on one of four-five widely used tree-to-dependency conversion schemes. This paper evaluates the down-stream effect of choice of conversion scheme, showing that it has dramatic impact on end results. 1
Emotional reactivity and the ability to modulate an emotional state, which are important factors for psychological well-being, are often dysregulated in psychiatric disorders. Neural correlates of emotional states have mostly been studied at the group level, thereby neglecting individual differences in the intensity of emotional experience. This study investigates the relationship between brain activity and interindividual variation in subjective affect ratings. A standardized mood induction (MI) procedure, using positive facial expression and autobiographical memories, was applied to 54 healthy participants (28 female), who rated their subjective affective state before and after the MI. We performed a regression analysis with brain activation during MI and changes in subjective affect ratings. An increase in positive affective ratings correlated with activity in the amygdala, hippocampus and the fusiform gyrus (FFG), whereas reduced positive affect correlated with activity of the subgenual anterior cingulate cortex. Activations in the amygdala, hippocampus and FFG are possibly linked to strategies adopted by the participants to achieve mood changes. Subgenual cingulate cortex activation has been previously shown to relate to rumination. This finding is in line with previous observations of the subgenual cingulate's role in emotion regulation and its clinical relevance to therapy and prognosis of mood disorders.
We present a discontinuous variant of tree-substitution grammar (tsg) based on Linear Context-Free Rewriting Systems. We use this formalism to instantiate a Data-Oriented Parsing model applied to discontinuous treebank parsing, and obtain a significant improvement over earlier results for this task. The model induces a tsg from the treebank by extracting fragments that occur at least twice. We give a direct comparison of a tree-substitution grammar implementation that implicitly represents all fragments from the treebank, versus one that explicitly operates with a significant subset. On the task of discontinuous parsing of German, the latter approach yields a 16 % relative error reduction, requiring only a third of the parsing time and grammar size. Fi-nally, we evaluate the model on several treebanks across three Germanic languages.
We explored the influence of implicit motives and activity inhibition (AI) on subjectively experienced affect in response to the presentation of six different facial expressions of emotion (FEEs; anger, disgust, fear, happiness, sadness, and surprise) and neutral faces from the NimStim set of facial expressions (Tottenham et al., 2009). Implicit motives and AI were assessed using a Picture Story Exercise (PSE) (Schultheiss et al., 2009b). Ratings of subjectively experienced affect (arousal and valence) were assessed using Self-Assessment Manikins (SAM) (Bradley and Lang, 1994) in a sample of 84 participants. We found that people with either a strong implicit power or achievement motive experienced stronger arousal, while people with a strong affiliation motive experienced less arousal and less pleasurable affect across emotions. Additionally, we obtained significant power motive × AI interactions for arousal ratings in response to FEEs and neutral faces. Participants with a strong power motive and weak AI experienced stronger arousal after the presentation of neutral faces but no additional increase in arousal after the presentation of FEEs. Participants with a strong power motive and strong AI (inhibited power motive) did not feel aroused by neutral faces. However, their arousal increased in response to all FEEs with the exception of happy faces, for which their subjective arousal decreased. These differentiated reaction patterns of individuals with an inhibited power motive suggest that they engage in a more socially adaptive manner of responding to different FEEs. Our findings extend established links between implicit motives and affective processes found at the procedural level to declarative reactions to FEEs. Implications are discussed with respect to dual-process models of motivation and research in motive congruence.
A major computational burden, while performing document clustering, is the calculation of similarity measure between a pair of documents. Similarity measure is a function that assigns a real number between 0 and 1 to a pair of documents, depending upon the degree of similarity between them. A value of zero means that the documents are completely dissimilar whereas a value of one indicates that the documents are practically identical. Traditionally, vector-based models have been used for computing the document similarity. The vector-based models represent several features present in documents. These approaches to similarity measures, in general, cannot account for the semantics of the document. Documents written in human languages contain contexts and the words used to describe these contexts are generally semantically related. Motivated by this fact, many researchers have proposed seman-tic-based similarity measures by utilizing text annotation through external thesauruses like WordNet (a lexical database). In this paper, we define a semantic similarity measure based on documents represented in topic maps. Topic maps are rapidly becoming an industrial standard for knowledge representation with a focus for later search and extraction. The documents are transformed into a topic map based coded knowledge and the similarity between a pair of documents is represented as a correlation between the common patterns (sub-trees). The experimental studies on the text mining datasets reveal that this new similarity measure is more effective as compared to commonly used similarity measures in text clustering.
Here we present an analysis of a 12-subject electroencephalographic (EEG) data set in which participants were asked to engage in prolonged, self-paced episodes of guided emotion imagination with eyes closed. Our goal is to correctly predict, given a short EEG segment, whether the participant was imagining a positive respectively negative-valence emotional scenario during the given segment using a predictive model learned via machine learning. The challenge lies in generalizing to novel (i.e., previously unseen) emotion episodes from a wide variety of scenarios including love, awe, frustration, anger, etc. based purely on spontaneous oscillatory EEG activity without stimulus event-locked responses. Using a variant of the Filter-Bank Common Spatial Pattern algorithm, we achieve an average accuracy of 71.3% correct classification of binary valence rating across 12 different emotional imagery scenarios under rigorous block-wise cross-validation.
We show that informative lexical categories from a strongly lexicalised formalism such as Combinatory Categorial Grammar (CCG) can improve dependency parsing of Hindi, a free word order language. We first describe a novel way to obtain a CCG lexicon and treebank from an existing dependency treebank, using a CCG parser. We use the output of a supertagger trained on the CCGbank as a feature for a state-of-the-art Hindi dependency parser (Malt). Our results show that using CCG categories improves the accuracy of Malt on long distance dependencies, for which it is known to have weak rates of recovery.
This document gives a brief description of Korean data prepared for the SPMRL 2013 shared task. A total of 27,363 sentences with 350,090 tokens are used for the shared task. All constituent trees are collected from the KAIST Treebank and transformed to the Penn Treebank style. All dependency trees are converted from the transformed constituent trees using heuristics and labeling rules de- signed specifically for the KAIST Treebank. In addition to the gold-standard morphological analysis provided by the KAIST Treebank, two sets of automatic morphological analysis are provided for the shared task, one is generated by the HanNanum morphological analyzer, and the other is generated by the Sejong morphological analyzer.
This chapter addresses the role played by language and schools in the history of Spain’s nineteenth-century liberal nation-building project. Both the Spanish language and the public school system were strategic sites where national consensus could be built and, consequently, the achievement of linguistic homogeneity through education became a central goal for the state. In particular, I examine the conditions that favored the linguistic norms developed by the Royal Spanish Academy and the debates that surrounded their officialization and imposition in the emerging national school system. While the historiography of Spanish has traditionally described the selection and implementation of the RAE’s norms as if they were undisputed and ideologically neutral, this study will emphasize the political complexity of the standardization process by approaching the archive with an ethnographic and historical-materialist perspective.
OBJECTIVE: Extreme emotional reactivity is a defining feature of borderline personality disorder, yet the neural-behavioral mechanisms underlying this affective instability are poorly understood. One possible contributor is diminished ability to engage the mechanism of emotional habituation. The authors tested this hypothesis by examining behavioral and neural correlates of habituation in borderline patients, healthy comparison subjects, and a psychopathological comparison group of patients with avoidant personality disorder. METHOD: During fMRI scanning, borderline patients, healthy subjects, and avoidant personality disorder patients viewed novel and repeated pictures, providing valence ratings at each presentation. Statistical parametric maps of the contrasts of activation during repeated versus novel negative picture viewing were compared between groups. Psychophysiological interaction analysis was employed to examine functional connectivity differences between groups. RESULTS: Unlike healthy subjects, neither borderline nor avoidant personality disorder patients exhibited increased activity in the dorsal anterior cingulate cortex when viewing repeated versus novel pictures. This lack of an increase in dorsal anterior cingulate activity was associated with greater affective instability in borderline patients. In addition, borderline and avoidant patients exhibited smaller increases in insula-amygdala functional connectivity than healthy subjects and, unlike healthy subjects, did not show habituation in ratings of the emotional intensity of the images. Borderline patients differed from avoidant patients in insula-ventral anterior cingulate functional connectivity during habituation. CONCLUSIONS: Unlike healthy subjects, borderline patients fail to habituate to negative pictures, and they differ from both healthy subjects and avoidant patients in neural activity during habituation. A failure to effectively engage emotional habituation processes may contribute to affective instability in borderline patients.
Fibromyalgia (FM) is characterized by widespread pain, as well as affective disturbance (eg, depression). Given that emotional processes are known to modulate pain, a disruption of emotion and emotional modulation of pain and nociception may contribute to FM. The present study used a well-validated affective picture-viewing paradigm to study emotional processing and emotional modulation of pain and spinal nociception. Participants were 18 individuals with FM, 18 individuals with rheumatoid arthritis (RA), and 19 healthy pain-free controls (HC). Mutilation, neutral, and erotic pictures were presented in 4 blocks; 2 blocks assessed only physiological-emotional reactions (ie, pleasure/arousal ratings, corrugator electromyography, startle modulation, skin conductance) in the absence of pain, and 2 blocks assessed emotional reactivity and emotional modulation of pain and the nociceptive flexion reflex (NFR, a physiological measure of spinal nociception) evoked by suprathreshold electric stimulations over the sural nerve. In general, mutilation pictures elicited displeasure, corrugator activity, subjective arousal, and sympathetic activation, whereas erotic pictures elicited pleasure, subjective arousal, and sympathetic activation. However, FM was associated with deficits in appetitive activation (eg, reduced pleasure/arousal to erotica). Moreover, emotional modulation of pain was observed in HC and RA, but not FM, even though all 3 groups evidenced modulation of NFR. Additionally, NFR thresholds were not lower in the FM group, indicating a lack of spinal sensitization. Together, these results suggest that FM is associated with a disruption of supraspinal processes associated with positive affect and emotional modulation of pain, but not brain-to-spinal cord circuitry that modulates spinal nociceptive processes.
OBJECTIVE: To develop, evaluate, and share: (1) syntactic parsing guidelines for clinical text, with a new approach to handling ill-formed sentences; and (2) a clinical Treebank annotated according to the guidelines. To document the process and findings for readers with similar interest. METHODS: Using random samples from a shared natural language processing challenge dataset, we developed a handbook of domain-customized syntactic parsing guidelines based on iterative annotation and adjudication between two institutions. Special considerations were incorporated into the guidelines for handling ill-formed sentences, which are common in clinical text. Intra- and inter-annotator agreement rates were used to evaluate consistency in following the guidelines. Quantitative and qualitative properties of the annotated Treebank, as well as its use to retrain a statistical parser, were reported. RESULTS: A supplement to the Penn Treebank II guidelines was developed for annotating clinical sentences. After three iterations of annotation and adjudication on 450 sentences, the annotators reached an F-measure agreement rate of 0.930 (while intra-annotator rate was 0.948) on a final independent set. A total of 1100 sentences from progress notes were annotated that demonstrated domain-specific linguistic features. A statistical parser retrained with combined general English (mainly news text) annotations and our annotations achieved an accuracy of 0.811 (higher than models trained purely with either general or clinical sentences alone). Both the guidelines and syntactic annotations are made available at https://sourceforge.net/projects/medicaltreebank. CONCLUSIONS: We developed guidelines for parsing clinical text and annotated a corpus accordingly. The high intra- and inter-annotator agreement rates showed decent consistency in following the guidelines. The corpus was shown to be useful in retraining a statistical parser that achieved moderate accuracy.
The ways in which literacy in English is taught in school generally subscribe to and perpetuate the notion of a homogenous, unvaried set of writing conventions associated with the language they represent, especially in relation to spelling and punctuation as well as grammar. Such teaching also perpetuates the myth that there is one correct way of language use which is fixed and invariant, and that any deviation is at best incorrect or illiterate and at worst, a threat to social stability. It is also very clear that the linguistic norms associated with standard English are predicated upon and replicate white, cultural hegemony. Yet, at the same time, there are plenty of literary and creative works written by authors from all kinds of different cultural, ethnic and linguistic backgrounds, including canonical ones, where spelling and punctuation are varied and championed as a sign of creativity. In the world beyond school, pupils are also surrounded by variational use of written language, especially in public displays such as shop signs, writing on mugs and t-shirts, posters, graffiti and so on, which link language to place. Equally, the voices we hear in entertainment and public broadcasting, far from being homogenous, celebrate diversity in Englishes. The homes and backgrounds of pupils in our schools, including their linguistic backgrounds, may also be very different either in terms of a different variation of English or languages spoken other than English. Since the emphasis is usually upon correct and fixed ways of teaching writing in English, it has often been difficult for teachers and pupils to reconcile the kind of English taught in school as the correct way and thus, by definition, all others as incorrect. However, narrow definitions of linguistic correctness are becoming increasingly difficult to uphold given that the public spaces with which we are surrounded are peppered by examples of variational use in writing. Recent sociolinguistic research into variation points to an increasing fluidity of linguistic use, especially when it comes to public displays of writing, particularly in media such as newspapers, websites, shop signs, TV channel logos and so on. Linguistic variability can thus be seen as a resource in creating unique voices and marking allegiance to, for example, a particular place and culture. Such research is indicative of the fact that variational use of English, far from being incorrect or illiterate, is increasingly being drawn upon creatively to mark a place identity. It also points to a shift in our conceptual thinking about language(s) and varieties from being perceived as static, fixed, totalised and immobile to being thought of as dynamic, fragmented and mobile, with the focus upon mobile resources rather than immobile languages. At the same time, the teaching of literacy centres upon the teaching of linguistic norms of spelling and grammar as fixed. There is a tension then, between creative expression of linguistic use often linked to place and those linked to standard English. This article explores those tensions and discusses the implications and possibilities for the teaching of English and literacy. © 2013.
This paper presents the validation of Preschool Competition Questionnaire (PCQ). The PCQ was completed by the childcare teachers of 780 French-speaking children between the ages of 36 and 71 months. The results of exploratory factor analysis suggest three dimensions involving neither physical nor relational aggression: other-referenced competition, task-oriented competition, and maintenance of dominance hierarchy. The three dimensions are positively correlated with dominance ratings and are linked to social adjustment. Girls are just as competitive as boys in the dimensions of other-referenced competition and dominance hierarchy maintenance. Task-oriented competition is relatively more important in older children and girls. Classification analysis reveals that the children who obtain the highest dominance ratings are the ones who employ a variety of competition strategies.
Dependency parsing algorithms capable of producing the types of crossing dependencies seen in natural language sentences have traditionally been orders of magnitude slower than algorithms for projective trees. For 95.8–99.8% of dependency parses in various natural language treebanks, whenever an edge is crossed, the edges that cross it all have a common vertex. The optimal dependency tree that satisfies this 1-Endpoint-Crossing property can be found with an O( n 4 ) parsing algorithm that recursively combines forests over intervals with one exterior point. 1-Endpoint-Crossing trees also have natural connections to linguistics and another class of graphs that has been studied in NLP.
In this article, we present a study of imageability ratings for a set of 1599 Norwegian words (896 nouns, 483 verbs and 220 adjectives) from a web-based survey. To a large extent, the results are in accordance with previous studies of other languages: high imageability scores in general, higher imageability scores for nouns than for verbs, and an inverse relation between frequency and imageability. A more surprising finding is the low imageability of low-frequency verbs. Also, imageability ratings increase systematically and significantly with informant age, reminding us that conceptual learning continues and changes throughout life. This has consequences for our expectations of different linguistic skills in a life span perspective. These findings have an obvious clinical relevance both for choice of items in test construction, for evaluation of performance in clinical groups and for development of therapy material.
linguistic normation in call centres and universities -how and why? aNNa kriStiNa hultgreN this article sets out to shed light on "linguistic normation" in two very different types of institutions in the globalized work order: call centres and universities."linguistic normation" is understood as metalinguistic practices aimed at making visible the linguistic and communicative behaviour of employees.Comparing examples from call centre customer service manuals and danish university language policies, it is argued that despite some obvious differences, the apparent urge in both workplaces to engage in "linguistic normation" is a revealing sign of the times in which we live.it is suggested that both institutions are characterized by a set of tensions which are a hallmark of the multilingual and competitive globalized work order.In the call centre, this tension is between rationalizing and providing a personalized service; in the university, it is between being a global actor and serving the domestic market.What is interesting, however, is that both institutions apparently regard language as the locus for solving these apparent tensions.it is argued that resorting to "linguistic normation" is a red herring which fails to identify and address the underlying political, economic, organizational and pedagogic factors which are the real causes of the perceived "problems" in these respective workplaces.
Abstract This experimental study investigates the impact of affective attitudes on risk and return estimates of stocks. Participants rate well‐known blue‐chip firms on an affective scale and forecast risk and return of the firms’ stock. We find that positive affective attitudes lead to a prediction of high return and low risk, while negative attitudes lead to a prediction of low return and high risk. This bias increases with participants’ confidence in their ratings and decreases with financial literacy. Firm characteristics such as a firm's marketing expenditures and the strength of its brand have a positive impact on its affective rating.
BACKGROUND: The experience of social exclusion represents an extremely aversive and threatening situation in daily life. The present study examined the impact of social exclusion compared to inclusion on steroid hormone concentrations as well as on subjective affect ratings. METHODS: Eighty subjects (40 females) participated in two independent behavioral experiments. They engaged in a computerized ball tossing game in which they ostensibly played with two other players who deliberately excluded or included them, respectively. Hormone samples as well as mood ratings were taken before and after the game. RESULTS: Social exclusion led to a decrease in positive mood ratings and increased anger ratings. In contrast, social inclusion did not affect positive mood ratings, but decreased sadness ratings. Both conditions did not affect cortisol levels. Testosterone significantly decreased after being excluded in both genders, and increased after inclusion, but only in males. Interestingly, progesterone showed an increase after both conditions only in females. DISCUSSION: Our results suggest that social exclusion does not trigger a classical stress response but gender-specific changes in sex hormone levels. The testosterone decrease after being excluded in both genders, as well as the increase after inclusion in males can be interpreted within the framework of the biosocial status hypothesis. The progesterone increase might reflect a generalized affiliative response during social interaction in females.
In assignment initially theorethical context of linguistic expressions is defined.First part continues to place the theory of language stratification, started in 1932 in Prague Linguistic Circle and in the second half of 20th century assumed in Slovene linguistic.First part also discusses about creation of slovene literary language and literary norm, continuing with arrangement of expressions in slovene theory of language stratification by Toporii (1971).Next to this contemporary definition of social stratification by Andrej E. Skubic ( 2005) is presented, concentrating on speeches of social groups (sociolects).Second part of assignment -based od research about elements of sociolects in discourse of slovene literature by Skubic (2006) and the concept of script by Roland Barthes (1971)places analised literary works, Fuinski bluz (Skubic, 2001) and efurji raus! (Vojnovi, 2008) into the fifth degree of development script as speech (Barthes, 1971).After presentation of literary works the analysis of communicative situations, where non-literary elements are used in speeches of all five literary figures, is discussed.At the beginning of the third part the treoretical description of reflextion of prague theory of linguistic stratification in SSKJ is presented.Moreover the presentation of activities during the time when SSKJ was published to SP 2001 is described, concluding with discussion about inclusion of linguistic stratification and sociolects in SP 2001.In the second, practical part linguistic analysis of elements of sociolects and speeches of literary figures are presented.The analysis is concentrated on lexical linguistic level and try to determine the relation between literary and non-literary language in works of contemporary slovene urban prose.
Volunteered geographic information (VGI) is generated by heterogenous ‘information communities’ that co-operate to produce reusable units of geographic knowledge. A consensual lexicon is a key factor to enable this open production model. Lexical definitions help demarcate the boundaries of terms, forming a thin semantic ground on which knowledge can travel. In VGI, lexical definitions often appear to be inconsistent, circular, noisy and highly idiosyncratic. Computing the semantic similarity of these ‘volunteered lexical definitions’ has a wide range of applications in GIScience, including information retrieval, data mining and information integration. This article describes a knowledge-based approach to quantify the semantic similarity of lexical definitions. Grounded in the recursive intuition that similar terms are described using similar terms, the approach relies on paraphrase-detection techniques and the lexical database WordNet. The cognitive plausibility of the approach is evaluated in the context of the OpenStreetMap (OSM) Semantic Network, obtaining high correlation with human judgements. Guidelines are provided for the practical usage of the approach.
The Montreal Affective Voices (MAVs) consist of a database of non-verbal affect bursts portrayed by Canadian actors, and high recognitions accuracies were observed in Canadian listeners. Whether listeners from other cultures would be as accurate is unclear. We tested for cross-cultural differences in perception of the MAVs: Japanese listeners were asked to rate the MAVs on several affective dimensions and ratings were compared to those obtained by Canadian listeners. Significant Group × Emotion interactions were observed for ratings of Intensity, Valence, and Arousal. Whereas Intensity and Valence ratings did not differ across cultural groups for sad and happy vocalizations, they were significantly less intense and less negative in Japanese listeners for angry, disgusted, and fearful vocalizations. Similarly, pleased vocalizations were rated as less intense and less positive by Japanese listeners. These results demonstrate important cross-cultural differences in affective perception not just of non-verbal vocalizations expressing positive affect (Sauter et al., 2010), but also of vocalizations expressing basic negative emotions.
Study of the situation in Lithuanian machine translation revealed, it is not possible to create the statistical machine translation system jet because of the lack of required amount of parallel texts. Thus the status of Lithuanian language is similar to that of the English language in the beginning of the computer – it is no sufficient computer resources. So we are now the best way to go which was the English language 50 years ago – to create rule-based machine translation system. The planed Treebank will serve creation of well working automatic syntactic analysis, which is needed for the rule-based machine translation.
Abstract This chapter presents the methods on which this book is based. Large quantities of data were generated from electronic sources. The most important sources used were the Corpus of Contemporary American English, the British National Corpus, various types of dictionary, websites and lexical databases. The chapter discusses the methodological problems with gathering and analyzing the data. Furthermore the conventions for citing data and for deciding which data to include are detailed. Problems of interpretation are also considered.
In our natural environment, emotional information is conveyed by converging visual and auditory information; multimodal integration is of utmost importance. In the laboratory, however, emotion researchers have mostly focused on the examination of unimodal stimuli. Few existing studies on multimodal emotion processing have focused on human communication such as the integration of facial and vocal expressions. Extending the concept of multimodality, the current study examines how the neural processing of emotional pictures is influenced by simultaneously presented sounds. Twenty pleasant, unpleasant, and neutral pictures of complex scenes were presented to 22 healthy participants. On the critical trials these pictures were paired with pleasant, unpleasant, and neutral sounds. Sound presentation started 500 ms before picture onset and each stimulus presentation lasted for 2 s. EEG was recorded from 64 channels and ERP analyses focused on the picture onset. In addition, valence and arousal ratings were obtained. Previous findings for the neural processing of emotional pictures were replicated. Specifically, unpleasant compared to neutral pictures were associated with an increased parietal P200 and a more pronounced centroparietal late positive potential (LPP), independent of the accompanying sound valence. For audiovisual stimulation, increased parietal P100 and P200 were found in response to all pictures which were accompanied by unpleasant or pleasant sounds compared to pictures with neutral sounds. Most importantly, incongruent audiovisual pairs of unpleasant pictures and pleasant sounds enhanced parietal P100 and P200 compared to pairings with congruent sounds. Taken together, the present findings indicate that emotional sounds modulate early stages of visual processing and, therefore, provide an avenue by which multimodal experience may enhance perception.
This package contains a partition of the Iula Spanish LSP Treebank into train and test sets to perform Machine Learning experiments. In that way the same partitions can be used by different researchers and their results can be directly compared. In this package we also deliver the Tibidabo Treebank (Marimon 2010) which contains a set of sentences extracted from Ancora corpus annotated in the same way than the Iula Treebank. Tibidabo Treebank is a very good test set for models trained with Iula Spanish LSP Treebank since the sentences that form it from a very different domain than those of the Iula Spanish LSP Treebank.
초록·키워드 목차 오류제보하기 本?文??在????的?程中，只有???准答案的束?，?生才能??自己的思???，展示?言表?能力和??。?文中?到的“故?”是在特定的?合下，有意出?的?言技巧，????非??，而是制造矛盾?到引??生思考的一?方式。在?????程中，故??含深意，能?迪?生的思?，?展?生的智力及?言表?能力，强化???容，?而提高?堂???量。 所?“?筋急??”是指?思?遇到特殊的阻碍?，不能按照原定思路思考，需要????路?，??的方面?思考??，?在泛指一些不能用通常的思路?回答的智力?答?。?筋急??巧妙地?用了??中的?音、??、?法以及文字、修?等?言?象。??益智??言游?，不?能?生喜?效果，??言表?含蓄幽默?趣，?能吸引?生激??生的???趣，使???得?松、?生?得愉快。 #汉语教学 #标准答案 #冒犯-故错 #反规范化 #脑筋急转 Ⅰ. 서론Ⅱ. 模範答案과 冒犯答案Ⅲ. 중국어의 ‘標準答案’과 탈규범화Ⅳ. 난센스 문답 속의 언어학Ⅴ. 결론참고문헌中文提要
Parser Evaluation using Textual Entailments (PETE) is a shared task in the SemEval-2010 Evaluation Exercises on Semantic Evaluation. The task involves recognizing textual entailments based on syntactic information alone. PETE introduces a new parser evaluation scheme that is formalism independent, less prone to annotation error, and focused on semantically relevant distinctions. This paper describes the PETE task, gives an error analysis of the top-performing Cambridge system, and introduces a standard entailment module that can be used with any parser that outputs Stanford typed dependencies.
Major depressive disorder (MDD) is associated with risk for chronic pain, but the mechanisms contributing to the MDD and pain relationship are unclear. To examine whether disrupted emotional modulation of pain might contribute, this study assessed emotional processing and emotional modulation of pain in healthy controls and unmedicated persons with MDD (14 MDD, 14 controls). Emotionally charged pictures (erotica, neutral, mutilation) were presented in 4 blocks. Two blocks assessed physiological-emotional reactions (pleasure/arousal ratings, corrugator electromyography (EMG), startle modulation, skin conductance) in the absence of pain and 2 blocks assessed emotional modulation of pain and the nociceptive flexion reflex (NFR, a physiological measure of spinal nociception) evoked by suprathreshold electric stimulations. Results indicated pictures generally evoked the intended emotional responses; erotic pictures elicited pleasure, subjective arousal, and smaller startle magnitudes, whereas mutilation pictures elicited displeasure, corrugator EMG activation, and subjective/physiological arousal. However, emotional processing was partially disrupted in MDD, as evidenced by a blunted pleasure response to erotica and a failure to modulate startle according to a valence linear trend. Furthermore, emotional modulation of pain was observed in controls but not MDD, even though there were no group differences in NFR threshold or emotional modulation of NFR. Together, these results suggest supraspinal processes associated with emotion processing and emotional modulation of pain may be disrupted in MDD, but brain to spinal cord processes that modulate spinal nociception are intact. Thus, emotional modulation of pain deficits may be a phenotypic marker for future pain risk in MDD.
Understanding, prioritizing and responding to infant affective cues is a key component of motherhood, with long-term implications for infant socio-emotional development. This important task includes identifying unique characteristics of one's own infant, as they relate to differences in affect valence-happy or sad-while monitoring one's own level of arousal. The amygdala has traditionally been understood to respond to affective valence; in the present study, we examined the potential effect of personal relevance on amygdala response, by testing whether mothers' amygdala response to happy and sad infant face cues would be modulated by infant identity. We used functional MRI to measure amygdala activation in 39 first-time mothers, while they viewed happy, neutral and sad infant faces of both their own and a matched unknown infant. Emotional arousal to each face was rated using the Self-Assessment Manikin Scales. Mixed-effects linear regression models were used to examine significant predictors of amygdala response. Overall, both arousal ratings and amygdala activation were greater when mothers viewed their own infant's face compared with unknown infant faces. Sad faces were rated as more arousing than happy faces, regardless of infant identity. However, within the amygdala, a highly significant interaction effect was noted between infant identity and valence. For own-infant faces, amygdala activation was greater for happy than sad faces, whereas the opposite trend was seen for unknown-infant faces. Our findings suggest that the amygdala response to positive or negative valenced cues is modulated by personal relevance. Positive facial expressions from one's own infant may play a particularly important role in eliciting maternal responses and strengthening the mother-infant bond.
The Treebanks as the sets of syntactically annotated sentences, are the most widely used language resource in the application of Natural Language Processing. The occurrence of errors in the automatically created Treebanks is one of the main obstacles limiting the using of these resources in the real world applications. This paper aims to introduce an statistical method for diminishing the amount of errors occurred in a specific English LTAG-Treebank proposed in Basirat and Faili (2013). The problem has been formulated as a classification problem and has been tackled by using several classifiers. The experiments show that by using this approach, about 95% of the errors could be detected and more than 77% of them could successfully be corrected in the case of using Adaboost classifier. In addition, it has been shown that the new treebank could reach a high of 76% F-measure which is 8% higher than the original treebank.
It has long been a dream to have available a single, centralized, semantic thesaurus or terminology taxonomy to support research in a variety of fields. Much human and computational effort has gone into constructing such resources, including the original WordNet and subsequent wordnets in various languages. To produce such resources one has to overcome well-known problems in achieving both wide coverage and internal consistency within a single wordnet and across many wordnets. In particular, one has to ensure that alternative valid taxonomizations covering the same basic terms are recognized and treated appropriately. In this paper we describe a pipeline of new, powerful, minimally supervised, automated algorithms that can be used to construct terminology taxonomies and wordnets, in various languages, by harvesting large amounts of online domain-specific or general text. We illustrate the effectiveness of the algorithms both to build localized, domain-specific wordnets and to highlight and investigate certain deeper ontological problems such as parallel generalization hierarchies. We show shortcomings and gaps in the manually-constructed English WordNet in various domains.
Short texts are typically composed of small number of words, most of which are abbreviations, typos and other kinds of noise. This makes the noise to signal ratio relatively high for this specific category of text. A high proportion of noise in the data is undesirable for analysis procedures as well as machine learning applications. Text normalization techniques are used to reduce the noise and improve the quality of text for processing and analysis purposes. In this work, we propose a combination of statistical and rule-based techniques to normalize short texts. More specifically, we focus our attention on SMS messages. We base our normalization approach on a statistical machine translation system which translates from noisy data to clean data. This system is trained on a small manually annotated set. Then, we study several automatic methods to extract more general rules from the normalizations generated with the statistical machine translation system. We illustrate the proposed methodology by conducting some experiments with a SMS Haitian-Créole data collection. In order to evaluate the performance of our methodology we use several Haitian-Créole dictionaries, the well-known perplexity criteria and the achieved reduction of vocabulary.
In this article, we examine the effectiveness of bootstrapping supervised machine-learning polarity classifiers with the help of a domain-independent rule-based classifier that relies on a lexical resource, i.e., a polarity lexicon and a set of linguistic rules. The benefit of this method is that though no labeled training data are required, it allows a classifier to capture in-domain knowledge by training a supervised classifier with in-domain features, such as bag of words, on instances labeled by a rule-based classifier. Thus, this approach can be considered as a simple and effective method for domain adaptation. Among the list of components of this approach, we investigate how important the quality of the rule-based classifier is and what features are useful for the supervised classifier. In particular, the former addresses the issue in how far linguistic modeling is relevant for this task. We not only examine how this method performs under more difficult settings in which classes are not balanced and mixed reviews are included in the data set but also compare how this linguistically-driven method relates to state-of-the-art statistical domain adaptation.
This paper describes our approaches to Na-tive Language Identification (NLI) for the NLI shared task 2013. NLI as a sub area of au-thor profiling focuses on identifying the first language of an author given a text in his sec-ond language. Researchers have reported sev-eral sets of features that have achieved rel-atively good performance in this task. The type of features used in such works are: lex-ical, syntactic and stylistic features, depen-dency parsers, psycholinguistic features and grammatical errors. In our approaches, we se-lected lexical and syntactic features based on n-grams of characters, words, Penn TreeBank (PTB) and Universal Parts Of Speech (POS) tagsets, and perplexity values of character of n-grams to build four different models. We also combine all the four models using an en-semble based approach to get the final result. We evaluated our approach over a set of 11 na-tive languages reaching 75 % accuracy. 1
In the field of constituency parsing, there exist multiple human-labeled treebanks which are built on non-overlapping text samples and follow different annotation standards. Due to the extreme cost of annotating parse trees by human, it is desirable to automatically convert one treebank (called source treebank) to the standard of another treebank (called target treebank) which we are interested in. Conversion results can be manually corrected to obtain higher-quality annotations or can be directly used as additional training data for building syntactic parsers. To perform automatic treebank conversion, we divide constituency parses into two separate levels: the part-of-speech (POS) and syntactic structure (bracketing structures and constituent labels), and conduct conversion on these two levels respectively with a feature-based approach. The basic idea of the approach is to encode original annotations in a source treebank as guide features during the conversion process. Experiments on two Chinese treebanks show that our approach can convert POS tags and syntactic structures with the accuracy of 96.6 and 84.8 %, respectively, which are the best reported results on this task.
Serial cognitive assessment is conducted to monitor changes in the cognitive abilities of patients over time. At present, mainly the regression-based change and the ANCOVA approaches are used to establish normative data for serial cognitive assessment. These methods are straightforward, but they have some severe drawbacks. For example, they can only consider the data of two measurement occasions. In this article, we propose three alternative normative methods that are not hampered by these problems—that is, multivariate regression, the standard linear mixed model (LMM), and the linear mixed model combined with multiple imputation (LMM with MI) approaches. The multivariate regression method is primarily useful when a small number of repeated measurements are taken at fixed time points. When the data are more unbalanced, the standard LMM and the LMM with MI methods are more appropriate because they allow for a more adequate modeling of the covariance structure. The standard LMM has the advantage that it is easier to conduct and that it does not require a Monte Carlo component. The LMM with MI, on the other hand, has the advantage that it can flexibly deal with missing responses and missing covariate values at the same time. The different normative methods are illustrated on the basis of the data of a large longitudinal study in which a cognitive test (the Stroop Color Word Test) was administered at four measurement occasions (i.e., at baseline and 3, 6, and 12 years later). The results are discussed and suggestions for future research are provided.
In this article, we validate an experimental paradigm, SPaM, that we first described elsewhere (Luke & Christianson, Memory & Cognition 40:628–641, 2012). SPaM is a synthesis of self-paced reading and masked priming. The primary purpose of SPaM is to permit the study of sentence context effects on early word recognition. In the experiment reported here, we show that SPaM successfully reproduces results from both the self-paced reading and masked-priming literatures. We also outline the advantages and potential uses of this paradigm. For users of E-Prime, the experimental program can be downloaded from our lab website, http://epl.beckman.illinois.edu/.
Researchers have long sought to distinguish between single-process and dual-process cognitive phenomena, using responses such as reaction times and, more recently, hand movements. Analysis of a response distribution’s modality has been crucial in detecting the presence of dual processes, because they tend to introduce bimodal features. Rarely, however, have bimodality measures been systematically evaluated. We carried out tests of readily available bimodality measures that any researcher may easily employ: the bimodality coefficient (BC), Hartigan’s dip statistic (HDS), and the difference in Akaike’s information criterion between one-component and two-component distribution models (AICdiff). We simulated distributions containing two response populations and examined the influences of (1) the distances between populations, (2) proportions of responses, (3) the amount of positive skew present, and (4) sample size. Distance always had a stronger effect than did proportion, and the effects of proportion greatly differed across the measures. Skew biased the measures by increasing bimodality detection, in some cases leading to anomalous interactive effects. BC and HDS were generally convergent, but a number of important discrepancies were found. AICdiff was extremely sensitive to bimodality and identified nearly all distributions as bimodal. However, all measures served to detect the presence of bimodality in comparison to unimodal simulations. We provide a validation with experimental data, discuss methodological and theoretical implications, and make recommendations regarding the choice of analysis.
Latencies of buttonpresses are a staple of cognitive science paradigms. Often keyboards are employed to collect buttonpresses, but their imprecision and variability decreases test power and increases the risk of false positives. Response boxes and data acquisition cards are precise, but expensive and inflexible, alternatives. We propose using open-source Arduino microcontroller boards as an inexpensive and flexible alternative. These boards connect to standard experimental software using a USB connection and a virtual serial port, or by emulating a keyboard. In our solution, an Arduino measures response latencies after being signaled the start of a trial, and communicates the latency and response back to the PC over a USB connection. We demonstrated the reliability, robustness, and precision of this communication in six studies. Test measures confirmed that the error added to the measurement had an SD of less than 1 ms. Alternatively, emulation of a keyboard results in similarly precise measurement. The Arduino performs as well as a serial response box, and better than a keyboard. In addition, our setup allows for the flexible integration of other sensors, and even actuators, to extend the cognitive science toolbox.
Discourse connectives play an important role in making a text coherent and helping humans to infer relations between spans of text. Using the Penn Discourse Treebank, we investigate what information relevant to inferring discourse relations is conveyed by discourse connectives, and whether the specificity of discourse relations reflects general cognitive biases for establishing coherence. We also propose an approach to measure the effect of a discourse marker on sense identification according to the different levels of a relation sense hierarchy. This will open a way to the computational modeling of discourse processing. 1