Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
18265 papers
We report work in progress on a complex system generating Czech sentences expressing the meaning of input syntactic-semantic structures. Such component is usually referred to as a realizer in the domain of Natural Language Generation. Existing realizers usually take advantage of a background linguistic theory. We introduce the Functional Generative Description, a framework of our choice conceived in 1960's by Petr Sgall. This language theory lays out foundations of the formalism in which our input syntactic-semantic structures are specified. The structure definition was further elaborated and refined during the annotation of the Prague Dependency Treebank, now available in its second version. A section of the paper is devoted to description of another theoretical framework suitable for the task of Natural Language Generation - the Meaning-Text Theory. We explore state-of-the-art realizers deployed in real life applications, describe common architecture of a generation system and highlight the strengths and weaknesses of our approach. Finally, preliminary output of our surface realizer is compared against a baseline solution.
We present an unsupervised linguistically-based approach to discourse relations recognition,\nwhich uses publicly available resources like manually annotated corpora (Discourse Graph\nBank, Penn Discourse TreeBank, RST-DT), as well as empirically derived data from “causally”\nannotated lexica like LCS, to produce a rule-based algorithm. In our approach we use\nthe subdivision of Discourse Relations into four subsets – CONTRAST, CAUSE, CONDITION,\nELABORATION, proposed by [1] in their paper where they report results obtained with a\nmachine-learning approach from a similar experiment against which we compare our results.\nOur approach is fully symbolic and is partially derived from the system called GETARUNS,\nfor text understanding, adapted to a specific task: recognition of Discourse Causal Relations\nin free text. We show that in order to achieve better accuracy both in the general task and in\nthe specific one, semantic information needs to be used besides syntactic structural information.\nOur approach outperforms results reported in previous papers
An alternative to the view that during evolution the human brain became specialized to preferentially attend to threat-related stimuli is to assume that all classes of stimuli that have high biological significance are prioritized by the attention system. Newborns are highly biologically relevant stimuli for members of a species, as their survival is important for reproductive success. The authors examined whether the Kindchenschema (baby schema) as described by Lorenz (1943) captures attention in the dot probe task. The results confirm attentional capture by photos of human infants presented to the left visual field, suggesting right hemisphere advantage. The magnitude of the attentional modulation was highly correlated with subjective arousal ratings of the photos. The findings show that biologically significant positive stimuli are prioritized by the attention system.
The current research explores the effects of exemplars on the stereotype representation of one's ingroup. Previous research demonstrated that exposure to an ingroup exemplar affects the stereotype one holds of one's ingroup (Coats & Smith, 1999). The primary purpose of the present study was to examine whether this effect is moderated by relative ingroup size. Participants were placed into either a minority or majority group situation and exposed to 1 of 2 dissimilar exemplars of their ingroup. Later, they rated their ingroup. Ratings of the ingroup differed between exemplar conditions in unexpected ways, indicating that the exemplar affected participants' stereotype of their ingroup. Furthermore, exemplars had a stronger effect on participants in the minority group than those in the majority group. Finally, relative ingroup size and, to a marginal extent, exemplars were found to affect ratings of ingroup variability.
Databases of hierarchically annotated text occupy a central place in linguistic research and language technology development. We describe a new approach to tree query which we call "Query by Annotation". Users express a query by annotating a tree, and the annotation is compiled into an expression in a path language. The result trees are overlaid with the original query, permitting the user to see why they match. Since queries and results are annotated trees, users can easily refine and resubmit their queries. The approach to Query by Annotation is motivated and exemplified using databases of linguistic trees, or treebanks.
Typically, personalized information recommendation services automatically infer a user profile, a structured model of the user interests, from documents the user already deemed as relevant. Traditional keyword-based approaches are unable to capture the semantics of the user interests. This work proposes a strategy consisting of two steps. The first one is a semantic indexing procedure based on a word sense disambiguation strategy which exploits the WordNet lexical database to select, among all the possible meanings (senses) of a polysemous word, the correct one. In the second step, semantically indexed documents are mined by a naive Bayes learning algorithm that infer semantic, sense-based user profiles. Two experimental sessions were carried out to compare the performance of keyword-based profiles to that of sense-based profiles. We measured both the classification accuracy and the effectiveness of the ranking imposed by the two different kinds of profile on the documents to be recommended. The main outcome of both experiments is that the classification accuracy is improved without improving the ranking. Personalized systems adapt their behavior to individual users by learning their preferences during the interaction in order to construct a user profile that can be later exploited in the search process. Traditional keyword-based approaches are primarily driven by a string-matching operation: If a string, or some morphological variant, is found in both the profile and the document, a match is made and the document is considered relevant. String matching suffers from problems of polysemy, the presence of multiple meanings for one word, and synonymy, multiple words having the same meaning. The result is that, due to synonymy, relevant information can be missed if the profile does not contain the exact
Models of social evaluation aim to capture the information people use to form first impressions of unfamiliar others. However, little is currently known about the relationship between perceived traits across gender. In Study 1, we asked viewers to provide ratings of key social dimensions (dominance, trustworthiness, etc.) for multiple images of 40 unfamiliar identities. We observed clear sex differences in the perception of dominance-with negative evaluations of high dominance in unfamiliar females but not males. In Study 2, we used the social evaluation context to investigate the key predictions about the importance of pictorial information in familiar and unfamiliar face processing. We compared the consistency of ratings attributed to different images of the same identities and demonstrated that ratings of images depicting the same familiar identity are more tightly clustered than those of unfamiliar identities. Such results imply a shift from image rating to person rating with increased familiarity, a finding which generalises results previously observed in studies of identification.
Semantic differential techniques are a useful, well-validated tool to assess affective processing of stimuli and determine how that processing is impacted by various demographic factors, such as gender. In this paper, we explore differences in connotative word processing between men and women as measured by Osgood's semantic differential and what those differences imply about affective processing in the two genders. We recruited 94 young participants (47 men, 47 women, ages 18-39) using an online survey and collected their affective ratings of 120 words on three rating tasks: Evaluation (E), Potency (P), and Activity (A). With these data, we explored the theoretical and mathematical overlap between Osgood's affective meaning factor structure and other models of emotional processing commonly used in gender analyses. We then used Osgood's three-dimensional structure to assess gender-related differences in three affective classes of words (words with connotation that is Positive, Neutral, or Negative for each task) and found that there was no significant difference between the genders when rating Positive words and Neutral words on each of the three rating tasks. However, young women consistently rated Negative words more negatively than young men did on all three of the independent dimensions. This confirms the importance of taking gender effects into account when measuring emotional processing. Our results further indicate there may be differences between Osgood's structure and other models of affective processing that should be further explored.
Objective To validate the translated Chinese version of Pain Assessment in Advanced Dementia Scale (C-PAINAD) for its clinical application in assessing the discomfort level of severely cognitive impaired patients. Methods In developing the C-PAINAD, both clinical and academic experts were engaged in an iterative translation process for ensuring its semantic equivalence with its original language as well as it comprehensibility for clinical application. In establishing C-PAINAD's inter-rater reliability, its applicability for clinical use is further assessed in 11 severely cognitive impaired patients. The assessment tools included C-PAINAD, Discomfort Visual Analog Scale (DVA), and Philadelphia Geriatric Center Affect Rating Scale (PGCAR). Correlations, ANOVA and factor analysis were undertaken to examine the reliability and validity of C-PAINAD. Results The scores of C-PAINAD were not in normal distribution but clustered around Zero. C-PAINAD was positively correlated with DVA and negative affect but mildly and negatively correlated with positive affect. It was able to detect different pain level under different condition. One factor was extracted and the percentage of variance was 51.20%. C-PAINAD was proved to have satisfactory reliability and validity(Cronbach's α=0.66). Conclusions C-PAINAD was a simple, reliable and effective pain assessment instrument for measuring pain level of non-communicative patients like advanced dementia. Further research is deemed necessary to conduct with pre - and post-test of analgesic prescriptions prior to wider clinical application.
The PARC 700 dependency bank is a potentially very useful resource for parser evaluation that has, so to speak, a high barrier to entry, because of tokenisation that is quite different from the source of the data, the Penn Treebank, and because there is no representation of word order, producing an uncertainty factor of some 15%. There is also a small, but perhaps not insignificant, number of errors. When using the dependency bank for evaluation, it seems likely that these things will cause inflated counts for mismatches, so to obtain more accurate measurements, it is desirable to eliminate them. The work reported here consists of an automatic conversion of the dependency bank into a Prolog representation where the word order is explicit, as well as graphical representations of the dependency trees for all 700 sentences, automatically generated from the Prolog data. As a side effect of the transformation, errors were detected and corrected. It is hoped that this work will lead to more widespread use of the PARC 700 dependency bank for parser evaluation.
Reviewed by: Indian and British English: A handbook of usage and pronunciationby Paroo Nihalni, R. K. Tongue, Priya Hosali, and Jonathan Crowther Niladri Sekhar Dash Indian and British English: A handbook of usage and pronunciation. 2ndedn. By Paroo Nihalni, R. K. Tongue, Priya Hosali, and Jonathan Crowther. New Delhi: Oxford University Press, 2004. Pp. x, 260. ISBN 0195666569. $15.95. The present handbook is divided into two main parts. The first part (‘Lexicon of usage’) is designed to provide English users with information about the way in which certain words, idioms, collocations, phrases, and similar expressions of English used in India differ from British Standard English (BSE)—a model that has the closest affinity to Indian English. This part includes a thousand English words, which are used in a distinctive manner by large numbers of educated Indian speakers of English irrespective of their place, profession, education, gender, or other sociolinguistic factors. The words included in the handbook are selected from the speech or writing samples of the persons (such as university and school teachers, journalists, and radio commentators) who are likely to influence the English use of Indian learners. The handbook also contains many European words that have been Indianized over the years. Thus, it serves as a handy resource for Indian speakers of English, illustrating the many, often quite subtle, ways in which Indian English differs from standard British English usage, and where these differences are regarded as acceptable or substandard in the subcontinent. Examples in the handbook, which supplement the texts, are helpful to Indian users of English who are uncertain about the ‘correctness’ of their speech and writing, and serve those scholars who want to explore the differences between Indian and British uses of English. The book also has the potential to address special problems faced by learners of English, who are often impeded by the difficulties of recognizing finer nuances of meaning and usage. The second part of the handbook includes a brief report on the development of the pronunciation dictionary in India and abroad, followed by insightful discussions on standards of pronunciation in second/ foreign language teaching, the phonological systems of the British Received Pronunciation (BRP) and Educated Indian English (EIE), and the role of supraseg-mental properties (i.e. word stress, sentence stress, rhythm, intonation, etc.) in Indian English. The introduction contains a list of keywords for phonetic symbols used in the following part, ‘Dictionary of pronunciation’. Two types of pronunciation (Indian Recommended Pronunciation and the BRP) are supplied for more than two thousand words collected from the original lexical database of Michael West’s General service list of English wordstogether with a few additions compiled from the language resources available to the compilers. Each entry of the dictionary is tagged with relevant phonological information. This second edition also includes additional information on lexical collocation (the tendency of words to be used together in fixed phrases). In essence, the handbook not only serves as an invaluable reference guide for students and teachers of English, but also makes a valuable contribution for applied linguists, lexicographers, journalists, and scholars who write in Indian English. [End Page 465] Niladri Sekhar Dash Indian Statistical Institute, Kolkata Copyright © 2007 Linguistic Society of America
As the Introduction to this volume observes, sixteenth-century France is marked by ‘une vaste réflexion sur le bien dire’. This not only impacted upon theory and practice across the different literary genres using French but also promoted considerable debate on the form and basis of the emerging standard form of the vernacular. Given the wide-ranging nature of this réflexion, covering its different manifestations in one volume poses an almost insuperable challenge, but the twenty-eight contributions to the colloquium collected here certainly address an ambitiously broad span of topics and add usefully to our understanding of cultural developments in a period of major change. The papers are organized into three general sub-sections, ‘Interroger la norme’, ‘Évolutions de la norme’ and ‘Normes et société’. However, such is the fluid nature of the subject matter treated in certain papers that their classification under one or other of these headings can sometimes seem of doubtful appropriateness. The focus of the first sub-section is predominantly literary. The various contributions address the creation or adaptation of norms across a considerable number of different genres some of which are perhaps rather less familiar, for instance, oracular writings (Dubois), accounts of pilgrimages (Gomez-Géraud) and Jesuit letter-writing (Laborie). Particularly interesting is the close study by Duché of the influential approach which Nicolas Herberay adopted for translation. Herberay, an acknowledged master of French prose (‘un vray Cicero françois’, according to Jean Martin), wrote with a female as well as a male readership in mind, developing a prose style that was eloquent and natural that would set an example for bien dire in this area. The second sub-section begins with a cogent overview (Baddeley) of a familiar field, developments in orthography and the interplay between orthography and spelling, and is followed by a series of studies which explore revealingly topics such as the evolving relationship between poetics and grammar (Monferran), the increasing limitation on the use of metaphor in literary works (Cernogora) and developments in historiography (Dumontet). Perhaps the most interesting paper is the examination of the fortunes of the alexandrine in the early part of the century (Halévy). Particular attention is given to the writings of Jean Lemaire de Belges and Geoffroy Tory both of whom, on the basis of fanciful argumentation, sought to invest the alexandrine with special prestige and nationalistic symbolism matching the terza rima in Italian. Their exercises in myth-making were to contribute indirectly, it is argued, to the rapid rise in the alexandrine's use from around 1555. The final sub-section of the volume contains contributions that more particularly address linguistic issues. Notable amongst these are two items: a re-evaluation of the system of vers mesurés devised by Baïf which is seen as an attempt not only to reproduce the metrical patterns of ancient Greek but also to contribute towards the norms of spoken French by reflecting the élite ‘usage des Bons’ (Vignes); and a meticulous examination by Morin of change in the pronunciation norms presented by Peletier du Mans in his earlier works (1550, 1555) as against his 1581 Euvres poëtiques, the new norm correlating with that presented later in the works of Lanoue (1596) and La Touche (1696). Alongside these are a number of other attractive essays including a study of the linguistic norms in the speeches made at the formal opening of the Paris Parlement, with eloquence and high rhetoric dominating over practicality and clarity between 1560 and 1600 before a reversal occurred in the early seventeenth century (Petey-Girard), and an investigation of sixteenth-century liminaires (any text preceding a written work) composed by women where a complex set of norms operate involving humility, simplicity of style, the practice of dedicating the work to another woman and, in the light of the lack of image for the female writer, an attempt to ‘socialiser l'auteur’ (Gauthier). Completing the text is an Index Nominum and a table of contents. The diversity and scholarly depth of the volume should ensure that all seiziémistes will derive benefit from a close reading.
Understanding user interests from text documents can provide support to personalized information recommendation services. Typically, these services automatically infer the user profile, a structured model of the user interests, from documents that were already deemed relevant by the user. Traditional keyword-based approaches are unable to capture the semantics of the user interests. This work proposes the integration of linguistic knowledge in the process of learning semantic user profiles that capture concepts concerning user interests. The proposed strategy consists of two steps. The first one is based on a word sense disambiguation technique that exploits the lexical database WordNet to select, among all the possible meanings (senses) of a polysemous word, the correct one. In the second step, a naïve Bayes approach learns semantic sensebased user profiles as binary text classifiers (userlikes and user-dislikes) from disambiguated documents. Experiments have been conducted to compare the performance obtained by keyword-based profiles to that obtained by sense-based profiles. Both the classification accuracy and the effectiveness of the ranking imposed by the two different kinds of profile on the documents to be recommended have been considered. The main outcome is that the classification accuracy is increased with no improvement on the ranking. The conclusion is that the integration of linguistic knowledge in the learning process improves the classification of those documents whose classification score is close to the likes / dislikes threshold (the items for which the classification is highly uncertain). 1
The Penn Treebank does not annotate within base noun phrases (NPs), committing only to flat structures that ignore the complexity of English NPs. This means that tools trained on Treebank data cannot learn the correct internal structure of NPs. This paper details the process of adding gold-standard bracketing within each noun phrase in the Penn Treebank. We then examine the consistency and reliability of our annotations. Finally, we use this resource to determine NP structure using several statistical approaches, thus demonstrating the utility of the corpus. This adds detail to the Penn Treebank that is necessary for many NLP applications.
Collocation is of great importance in dictionary compilation and natural language processing.Collocation extraction is one of the principal applications of corpus linguistics.Automatic extraction of bi-grams as candidate collocations is studied on Penn Treebank using the criteria of log likelihood,chi square and mutual information as association measure.The experimental results show the feasibility of the statistical methods.On the other hand,collocations extracted show different characteristics because of the different distribution assumptions by the three criteria.
An improved k-means clustering method is proposed to identify Chinese phrases with the purpose of avoiding data sparseness and taking think of the relationship of neighbor part of speech and the cohesion of all part of speeches within one phrase.The proposed method regards each phrase as a cluster whose kernel is headword,which richly used the constituent disciplinarian of one phrase.It also integrates supervised statistical method and unsupervised clustering method by setting the original center of each class according the data from small Chinese corpus,which not only improves the accuracy of clustering but also avoids data sparseness.Through testing on Chinese Penn Treebank, the F score of seven types of Chinese phrase achieves to 92.94%.So,it is effective for Chinese text chunking.
This paper presents a semi-automatic approach for extraction of collocations from corpora which uses the results of Conceptual Vectors as a semantic filter. First, this method estimates the ability of each co-occurrence to be a collocation, using a statistical measure based on the fact that it occurs more often than by chance. Then the results are automatically filtered (with conceptual vectors) to retain only one given semantic kind of collocations. Finally we perform a new filtering based on manually entered data. Our evaluation on monolingual and bilingual experiments shows the interest to combine automatic extraction and manual intervention to extract collocations (to fill multilingual lexical databases). It proves especially that the use of conceptual vectors to filter the candidates allows us to increase the precision noticeably.
In this paper, we address the issue of improving a Chinese chunking system with rich lexicalized information. A method that incorporates statistical information based on distributional similarity between words obtained from large unlabeled corpus and morphological knowledge into a state-of-the-art CRF-based chunking model is proposed to tackle the data sparseness problem given limited amount of labeled training data. Evaluations are performed on the latest release of Chinese Treebank, and experimental results show that our method outperforms the chunking models based on features over word and automatically assigned POS tags when using the same amount of training data.
The paper aims at the complexity of syntactic network and the feasibility that the complex network work as a means of linguistic studies.The paper proposes the method how to build a syntactic based on dependency treebank and investigates the complexity of Chinese syntactic dependency network based on two Chinese treebanks with different genres.The results show that syntactic networks have similar average path length and diameter with the random networks,but cluster coefficients of syntactic networks are much greater than that of random networks,and degree distributions of syntactic networks also obey the power law.The paper reveals that two syntactic networks have the same diameter,but with different average degree,path length,cluster coefficients and power exponent.
This paper proposes a novel Chinese syntactic parsing model based on semantic class, which is a variant of normal lexicalized statistical model. It attempts to make use of the syntactic and semantic similarity between Chinese words and then produces a more knowledgeable estimate of the probability of grammar rules. A simple but effective unsupervised method is designed to determine the proper semantic class of given words. Semantic class is used to improve the performance of parsing model. We evaluate our methods on the widely used Penn Chinese Treebank. Experimental results show that it outperforms a famous lexicalized model significantly on appropriate semantic class levels.
Lists of names are an important knowledge source for many systems which carry out named entity recognition. It is shown that augmenting hand-crafted lists with those derived from corpora can improve their performance. Two methods for improving automatically acquired lists are presented. The best corpus-derived lists are shown to out-perform the hand-crafted ones by 4%. 1. Introduction Named entity (NE) recognition is the process of identifying and categorising names in text. NE recognition and corpora research can be mutually benficial. Corpora are often more valuable when linguistic information has been added to them. For example the SUZANNE and Penn TreeBank corpora contain texts which have been parsed and as a consequence these corpora are widely used resources in NLP research. In a similar fashion NE recognition can be used to annotate the names in texts and thereby produce a richer corpus. Conversely, the information in annotated corpora can be very useful in the development of...
Treebank data have been utilized as data sources for a wide range of tasks in computational linguistics, including statistical parsing, anaphora resolution, induction of valence lexica, etc. More recently, researchers have experimented with extracting semantic information from syntactically annotated data. Here, treebank data
This paper describes our attempt at NomBank-based automatic Semantic Role Labeling (SRL). NomBank is a project at New York University to annotate the argument structures for common nouns in the Penn Treebank II corpus. We treat the NomBank SRL task as a classification problem and explore the possibility of adapting features previously shown useful in PropBank-based SRL systems. Various NomBank-specific features are explored. On test section 23, our best system achieves F1 score of 72.73 (69.14) when correct (automatic) syntactic parse trees are used. To our knowledge, this is the first reported automatic NomBank SRL system.
Literature suggests that relatively simple stimuli such as emotional facial expressions elicit neural activation in subcortical-limbic regions whereas contextually richer emotional pictures generate activation in a broader network of prefrontal as well as subcortical-limbic regions. The extent to which contextual features modulate subjective and neural responses associated with responses to emotional faces is unclear. Normative valence and arousal ratings for a large corpus of affective pictures (IAPS) were reviewed to explore whether emotional pictures containing both faces and context evoked more intense subjective emotional reactions than faces presented alone. This review study demonstrated that subjective emotional reactions to emotional faces with contextual information were greater than those to faces. An fMRI study was conducted to examine neural reactivity to these two types of emotional stimuli. Eleven healthy right-handed subjects viewed passively emotional stimuli during event-related functional magnetic resonance imaging (fMRI) assessment. Emotional faces augmented by contextual information elicited significant brain activity in the prefrontal cortex (BA10/11/47) as well as amygdala and thalamus. In contrast, emotional facial expressions provoked neural responses only in the subcortical-limbic/paralimbic regions including amygdala, thalamus, insula and posterior cingulate gyrus. These findings suggest that there are different but overlapping brain networks engaged by emotional faces and faces augmented by contextual information. The amygdala and thalamus can be regarded as common regions associated with emotional processing. Prefrontal regions may be unique in more cognitive and conscious processing of emotional faces augmented by contextual information.
The Dutch spelling system, like other European spelling systems, represents a certain balance between preserving the spelling of morphemes (the morphological principle) and obeying letter-to-sound regularities (the phonological principle). We present experimental results with artificial learners that show a competition effect between the two principles: adhering more to one principle leads to more violations of the other. The artificial learners, memory-based learning algorithms, are trained (1) to convert written words to their phonemic counterparts and (2) to analyze written words on their morphological composition, based on data extracted from the CELEX lexical database. As an exception to the competition effect we show that introducing the schwa as a letter in the spelling system causes both morphology and phonology to be learnt better by the artificial learners. In general we argue that artificial learning studies are a tool in obtaining objective measurements on a spelling system that may be of help in spelling reform processes.
Scary picture has little effect on violent video fans. CREDITS: BRADLEY LANG AND B. N. CUTHBERT, INTERNATIONAL AFFECTIVE PICTURE SYSTEM (IAPS): INSTRUCTION MANUAL AND AFFECTIVE RATINGS (UNIVERSITY OF FLORIDA, 2001)
This paper presents an approach to dependency parsing which can utilize any standard machine learning (classification) algorithm. A decision list learner was used in this work. The training data provided in the form of a treebank is converted to a format in which each instance represents information about one word pair, and the classification indicates the existence, direction, and type of the link between the words of the pair. Several distinct models are built to identify the links between word pairs at different distances. These models are applied sequentially to give the dependency parse of a sentence, favoring shorter links. An analysis of the errors, attribute selection, and comparison of different languages is presented.
Since ancient linguistics, the studies of Indo-European word order work with the conception of universal natural word order (ordo naturalis) – an order of the verb-dependent constituents in the linear organization of a clause. The description of the natural word order is usually based on occasional (and in some degree random) observations of clauses in a certain language. In Czech linguistics, the idea of the natural word order was formulated in a more precise way as the hypothesis of the systemic ordering (Sgall, Hajicova and Buraňova, 1980). According to the authors, the contextually non-bound participants and adverbials are ordered as follows (o. c., page 77):
Data-driven grammatical function tag assignment has been studied for English using the Penn-II Treebank data. In this paper we address the question of whether such methods can be applied successfully to other languages and treebank resources. In addition to tag assignment accuracy and f-scores we also present results of a task-based evaluation. We use three machine-learning methods to assign Cast3LB function tags to sentences parsed with Bikel's parser trained on the Cast3LB treebank. The best performing method, SVM, achieves an f-score of 86.87% on gold-standard trees and 66.67% on parser output - a statistically significant improvement of 6.74% over the baseline. In a task-based evaluation we generate LFG functional-structures from the function-tag-enriched trees. On this task we achive an f-score of 75.67%, a statistically significant 3.4% improvement over the baseline.
OBJECTIVE: We examined whether affect ratings predicted regional cerebral responses to high and low-calorie foods. METHOD: Thirteen normal-weight adult women viewed photographs of high and low-calorie foods while undergoing functional magnetic resonance imaging (fMRI). Regression analysis was used to predict regional activation from positive and negative affect scores. RESULTS: Positive and negative affect had different effects on several important appetite-related regions depending on the calorie content of the food images. When viewing high-calorie foods, positive affect was associated with increased activity in satiety-related regions of the lateral orbitofrontal cortex, but when viewing low-calorie foods, positive affect was associated with increased activity in hunger-related regions including the medial orbitofrontal and insular cortex. The opposite pattern of activity was observed for negative affect. CONCLUSION: These findings suggest a neurobiologic substrate that may be involved in the commonly reported increase in cravings for calorie-dense foods during heightened negative emotions.
The present paper proposes a method by which to translate outputs of a robust HPSG parser into semantic representations of Typed Dynamic Logic (TDL), a dynamic plural semantics defined in typed lambda calculus. With its higher-order representations of contexts, TDL analyzes and describes the inherently inter-sentential nature of quantification and anaphora in a strictly lexicalized and compositional manner. The present study shows that the proposed translation method successfully combines robustness and descriptive adequacy of contemporary semantics. The present implementation achieves high coverage, approximately 90%, for the real text of the Penn Treebank corpus.
This paper describes a featurized functional dependency corpus automatically derived from the Penn Treebank. Each word in the corpus is associated with over three dozen features describing the functional syntactic structure of a sentence as well as some shallow morphology. The corpus was created for use in probabilistic surface generation, but could also be useful as a resource for the study of English and the development of other NLP applications. 1.
Chinese word segmentation and Part-of-Speech (POS) tagging have been commonly considered as two separated tasks. In this paper, we present a system that performs Chinese word segmentation and POS tagging simultaneously. We train a segmenter and a tagger model separately based on linear-chain Conditional Random Fields (CRF), using lexical, morphological and semantic features. We propose an approximated joint decoding method by reranking the N-best segmenter output, based POS tagging information. Experimental results on SIGHAN Bakeoff dataset and Penn Chinese Treebank show that our reranking method significantly improve both segmentation and POS tagging accuracies. 1
This paper describes a parser which generates parse trees with empty elements in which traces and fillers are co-indexed. The parser is an unlexicalized PCFG parser which is guaranteed to return the most probable parse. The grammar is extracted from a version of the PENN treebank which was automatically annotated with features in the style of The annotation includes GPSG-style slash features which link traces and fillers, and other features which improve the general parsing accuracy. In an evaluation on the PENN treebank Its results for the empty category prediction task and the trace-filler coindexation task exceed all previously reported results with 84.1% and 77.4% fscore, respectively.
We present a two stage parser that recovers Penn Treebank style syntactic analyses of new sentences including skeletal syntactic structure, and, for the first time, both function tags and empty categories. The accuracy of the first-stage parser on the standard Parseval metric matches that of the (Collins, 2003) parser on which it is based, despite the data fragmentation caused by the greatly enriched space of possible node labels. This first stage simultaneously achieves near state-of-the-art performance on recovering function tags with minimal modifications to the underlying parser, modifying less than ten lines of code. The second stage achieves state-of-the-art performance on the recovery of empty categories by combining a linguistically-informed architecture and a rich feature set with the power of modern machine learning methods.
Standard techniques used in multilingual terminology management fail to describe legal terminologies as they are bound to different legal systems and terms do not share a common meaning. In the LexALP project, we use a technique defined for general lexical databases to achieve cross language interoperability between languages of the Alpine Convention. In this paper we present the methodology and tools developed for the collection, description and harmonisation of the legal terminology of spatial planning and sustainable development in the four languages of the countries of the Alpine Space.
Group identifications and intergroup relations among Turkish Dutch respondents What determines group identification processes among ethnic minority groups and how are these processes related to in-group and out-group evaluations? This article focuses on Turkish and Dutch identification among Turkish Dutch respondents and their feelings towards different ethnic and religious groups. The results show that Turkish identification is strong and Dutch identification rather weak, and that both group identifications are not strongly associated. Perceived socio-structural characteristics of intergroup relations (stability, legitimacy, permeability, and discrimination) affected both Turkish and Dutch identification. Group identification was positively related to (ethnic and religious) in-group evaluation, but there were few relationships with out-group evaluations. The affective ratings of Moroccans, Antilleans, Jews and non-believers were quite negative.
The suitability of computer‐based instruction (CBI) for workers with limited education was evaluated in an Hispanic orchard workforce that reported little computer experience and 5.6 mean years of formal education. Ladder safety training was completed by employees who rated the training highly (effect size [d_gain] = 5.68), and their knowledge of ladder safety improved (d_gain = 1.45). There was a significant increase (p < 0.01) in safe work practices immediately after training (d_gain = 0.70), at 40 days post training (d_gain = 0.87) and at 60 days (d_gain = 1.40), indicating durability. As in mainstream populations, reaction or affective ratings correlated well with utility ratings, but not with behavior change. This demonstrates that an agricultural workforce with limited formal education can learn job safety from CBI and translate the knowledge to work practice changes, and those changes are durable.
We introduce MaltParser, a data-driven parser generator for dependency parsing. Given a treebank in dependency format, MaltParser can be used to induce a parser for the language of the treebank. MaltParser supports several parsing algorithms and learning algorithms, and allows user-defined feature models, consisting of arbitrary combinations of lexical features, part-of-speech features and dependency features. MaltParser is freely available for research and educational purposes and has been evaluated empirically on Swedish, English, Czech, Danish and Bulgarian. 1.
Previous articleNext article FreeCurrent ApplicationsLinguistic AnthropologyV.ChandV.Chand Search for more articles by this author PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinked InRedditEmailQR Code SectionsMoreImmigration Practices in Belgium: African AsylumSeeker DiscourseJan Blommaert, a Belgian anthropologist currently at the Institute of Education, University of London, is known in Belgium as a public campaigner on immigration issues. He became involved in African asylumseekers rights in 1998, when the death of Semira Adamu during her forced repatriation provoked a public outcry over the implementation of Belgian immigration policies, in which more than 95% of applicants for asylum are rejected. Adamu had fled Nigeria in her teens to avoid entering into a polygamous marriage with a 65yearold man. Shackled at the ankles and vigorously resisting, she was suffocated by Belgian police attempting to restrain her.Poster for a 2003 commemoration of Adamu's death.View Large ImageDownload PowerPointAdamus death was a catalyst for the formation of new action groups, and existing organizations also became involved: churches opened their doors to asylum seekers, and NGOs such as OXFAM and the League of Human Rights campaigned for asylum seekers rights. Early on, Blommaert contributed to this organic collaborative effort: I gave tons of public lectures for any audience willing to listen, wrote opeds in major newspapers, campaigned with MPs close to the government, participated in public debates on these matters, and wrote expert articles for a wider audience. Additionally, he saw that his understanding of the legal and sociolinguistic issues was directly applicable to the problem. He was motivated, he explains, by awareness of the real stakes and real people involved.African asylum seekers face circumstances not shared with those from Europe and the Gulf because they come from wartorn areas with unclear state boundaries, may be illiterate and lack documentation, have long migration paths to Belgium, and do not share a language with immigration authorities. In particular, Blommaert points out, their choices of language, background texts, and genres for storytelling affect their chances of acquiring refugee status. African asylumseeker language is stereotypically filled with language mixing, language impurities, varying degrees of literacy, and different styles of storytelling and discourse. When asylum seekers present their stories, they are unaware of how the officials are judging them on their linguistic habits. The officials note these details and find them inconsistent with Belgian expectations; they judge the asylum seekers in terms of these nave choices and reject them. In many such cases, rejection is a matter of life or death for the refugees, given the risks associated with deportation and with repatriation into the home country that originally motivated them to seek asylum.Blommaert has mobilized an informal network of Belgian academics and institutions that has produced many academically informed public statements. Additionally, by documenting and analyzing oral immigration interviews with an eye to understanding the disconnect between asylum seekers presentations and immigration officials expectations, he has improved practice in the Immigration Department. He has used his analysis to train members of the department, helping to adjust views of what can be gathered in an immigration interview, to improve interview techniques, and to promote an awareness of variability in sociocultural linguistic norms and presentation styles.He continues to work as an official expert for Belgian legal, government, and security authorities, translating and analyzing documentation and corroboratory written texts provided for immigration procedures and advising on specific issues. He has been able to influence outcomes for some individual asylum seekers. His public campaigning has raised awareness of the issues surrounding African asylum seekers, and his publications have provoked work within academia that may lead to further interventions by academics with regard to the interview process.Blommaert hopes that public campaigning by NGOs and academic scholarship will promote continued dialogue with immigration officials, eventually producing policies and procedures that take into account the politics of migration and displacement and the way in which asylum seekers frame their life stories. Previous articleNext article DetailsFiguresReferencesCited by Current Anthropology Volume 47, Number 3June 2006 Sponsored by the Wenner-Gren Foundation for Anthropological Research Article DOIhttps://doi.org/10.1086/504161 Views: 287Total views on this site Citations: 1Citations are reported from Crossref PDF download Crossref reports the following articles citing this article:Kevin D. 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