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18265 papers
A statistical estimator attempts to guess an unknown probability distribution by analyzing a sample from this distribution. One desirable property of an estimator is that its guess is increasingly likely to get arbitrarily close to the actual distribution as the sample size increases. This property is called consistency. Data Oriented Parsing (DOP) employs all fragments of the trees in a training treebank, including the full parse-trees themselves, as the rewrite rules of a probabilistic tree-substitution grammar. Since the most popular DOP-estimator (DOP1) was shown to be inconsistent, there is an outstanding theoretical question concerning the possibility of DOP-estimators with reasonable statistical properties. This question constitutes the topic of the current paper. First, we show that, contrary to common wisdom, any unbiased estimator for DOP is futile because it will not generalize over the training treebank. Subsequently, we show that a consistent estimator that generalizes over the treebank should involve a local smoothing technique. This exposes the relation between DOP and existing memory-based models that work with full memory and an analogical function such as k-nearest neighbor, which is known to implement backoff smoothing. Finally, we present a new consistent backoff-based estimator for DOP and discuss how it combines the memory-based preference for the longest match with the probabilistic preference for the most frequent match.
This paper presents a prosodic phrasing model for Korean to be used in a text-to-speech synthesis (TTS) system. Read text corpora were morpho-syntactically parsed and prosodically labeled following the Penn Korean Treebank (Han, Chunghye, Ko, Eon-Suk, Yi, Heejong, Palmer, M., 2002. Penn Korean Treebank: development and evaluation. In: Proceedings of the 16th Pacific Asian Conference on Language and Computation. Korean Society for Language and Information.) and K-ToBI prosodic labeling conventions (Sun-Ah, J., 2000. K-ToBI (Korean ToBI) labelling conventions. Version 3.1. Available from: URL.), respectively. Decision trees were trained with morpho-syntactic and textual distance features to predict locations of accentual and intonational phrase breaks. Our phrasing model cross-validated on a 300-sentence corpus (6936 words or 21,436 syllables, with an average of 72 syllables or 23 words per sentence) predicted non-breaks with F=92.4% and breaks with F=88.0% (F=72.8% for accentual phrase breaks and F=71.3% for intonational phrase breaks).
The present work falls in the line of activities promoted by the European Languguage Resource Association (ELRA) Production Committee (PCom) and raises issues in methods, procedures and tools for the reusability, creation, and management of Language Resources. A two-fold purpose lies behind this experiment. The first aim is to investigate the feasibility, define methods and procedures for combining two Italian lexical resources that have incompatible formats and complementary information into a Unified Lexicon (UL). The adopted strategy and the procedures appointed are described together with the driving criterion of the merging task, where a balance between human and computational efforts is pursued. The coverage of the UL has been maximized, by making use of simple and fast matching procedures. The second aim is to exploit this newly obtained resource for implementing the phonological and morphological layers of the CLIPS lexical database. Implementing these new layers and linking them with the already exisitng syntactic and semantic layers is not a trivial task. The constraints imposed by the model, the impact at the architectural level and the solution adopted in order to make the whole database ‘speak ’ efficiently are presented. Advantages vs. disadvantages are discussed. 1. Background and Motivations The work described here raises issues in methods, procedures and tools for the reusability, creation, and management of Language Resources (LRs) and has been
This paper explores the interaction between conceptual structure and morpho-syntax. In particular, we show that ontology-based conceptual classification can be used to predict internal relations in compounds. We propose an ontology-based approach to predict the semantic relation between the two component words in Mandarin VV compounds. A Mandarin VV compound is classified according to the eventive relation between the two simplex verbs. These relations specify how the eventive meanings of the two simplex verbs combine to form the meaning of the compound. The three types of eventive relations that we deal with in this paper are: coordinate, modificational, and resultative. Since the way in which two events combine with each other depends upon their event types, we hypothesize that the eventive relations can be predicted by the conceptual classified event types of the two simplex verbs. An approach of ontology-based prediction is proposed based on this hypothesis. The assignment of ontology classification for each simplex verb is based on SUMO and Sinica BOW. The correlation between the ontology class of each verb position and each eventive type is trained and scored based on a manually tagged lexical database. We encode the ontology information of each VV compound in a 3-tuple based on these correlation scores. This 3-tuple is represented as a three-dimensional vector and used to predict the eventive type of new VV compounds. Our classification experiment on unknown VV compounds yields good recall and precision. 1.
To assign semantic roles in building Treebanks, there is a need for annotators having a guideline in determining semantic relations between phrasal head and its modifiers or arguments. Semantic roles are hard to have clear-cut definitions. It is not always easy to determine thematic relations between two concepts. This paper aims to introduce an integrated nominal modifier system. Basically we adopt other scholars ' incisive idea in analyzing semantic roles that modify general nouns. We use the approach of building a fine-grain taxonomy of role system. The taxonomy of fine-grain thematic roles makes the role determination easier for human annotators, since the meaning of a fine-grain semantic role is self explanatory and a higher-level semantic role is described by its hyponyms. The proposed taxonomy has been attested during construction of Sinica TreeBank and HowNet definitions of nominal concepts and proven to be more applicable than conventional flat structures. 1
Recent evaluation techniques applied to corpus-based systems have been introduced that can predict quantitatively how well surface realizers will generate unseen sentences in isolation. We introduce a similar method for determining the coverage on the Fuf/Surge symbolic surface realizer, report that its coverage and accuracy on the Penn TreeBank is higher than that of a similar statistics-based generator, describe several benefits that can be used in other areas of computational linguistics, and present an updated version of Surge for use in the NLG community.
The current research explored the processes that predominate during the anticipation of an emotionally salient event. Experiment 1 (N536), employed three different conditional stimuli followed by pictorial pleasant, unpleasant or neutral unconditioned stimuli. Half the participants were trained with visual CSs, the other half with tactile CSs. In the group trained with visual CSs, startle eyeblinks were larger and faster during CSs that were paired with unpleasant pictures than CSs paired with neutral or pleasant pictures respectively, indicating an affect startle pattern. This linear trend was not found in the group trained with tactile CSs. Experiment 2 (N564) aimed to investigate whether the affective pattern found in the startle data in Experiment 1 could also be found using a behavioural measure of emotion. This time participants’ reaction time during a post-experimental affective priming taskwas used as dependantmeasure to assess the presence of emotional learning. Instead of a simple differential conditioning task, an occasion setting paradigm was employed and participants were trained using either a feature positive or feature negative design with pleasant or unpleasant picture USs. For participants trained with unpleasant USs, valence ratings collected before and after conditioning training suggested the presence of emotional learning, whereas no such pattern was found for participants trained with pleasant USs. These findings were not confirmed in the priming data.
Automatic extraction and reasoning over temporal properties in natural language discourse has not had wide use in practical systems due to its demand for a rich and compositional, yet inference-friendly, representation of time. Motivated by our study of temporal expressions from the Penn Treebank corpora, we address the problem by proposing a two-level constraint-based framework for processing and reasoning over temporal information in natural language. Within this framework, temporal expressions are viewed as partial assignments to the variables of an underlying calendar constraint system, and multiple expressions together describe a temporal constraint-satisfaction problem (TCSP). To support this framework, we designed a typed formal language for encoding natural language expressions. The language can cope with phenomena such as under-specification and granularity change. The constraint problems can be solved using various constraint propagation and search methods, and the solutions can then be used to answer a wide range of time-related queries.
We present a method to approximate a LTAG grammar by a CFG. A key process in the approximation method is finite enumeration of partial parse results that can be generated during parsing. We applied our method to the XTAG English grammar and LTAG grammars which are extracted from the Penn Treebank, and investigated characteristics of the obtained CFGs. We perform CFG filtering for LTAG by the obtained CFG. In the experiments, we describe that the obtained CFG is useful for CFG filtering for LTAG parser. 1
The method of organization of word meanings is a crucial issue with lexical databases. Our purpose in this research is to extract word hierarchies from corpora automatically. Our initial task to this end is to determine adjective hyperonyms. In order to find adjective hyperonyms, we utilize abstract nouns. We constructed linguistic data by extracting semantic relations between abstract nouns and adjectives from corpus data and classifying abstract nouns based on adjective similarity using a self-organizing semantic map, which is a neural network model (Kohonen 1995). In this paper we describe how to hierarchically organize abstract nouns (adjective hyperonyms) in a semantic map mainly using CSM. We compare three hierarchical organizations of abstract nouns, according to CSM, frequency (Tf.CSM) and an alternative similarity measure based on coefficient overlap, to estimate hyperonym relations between words.
In information retrieval and text mining, information on word senses is usually taken from dictionaries or lexical databases that have been prepared by lexicographers. We propose an automatic method for word sense induction, i.e. for the discovery of a set of sense descriptors to a given ambiguous word. The approach is based on the statistics of word co-occurrence as derived from Web pages. The underlying assumption is that the senses of an ambiguous word are best described by terms that, although bearing a strong association to this word, are mutually exclusive, i.e. whose association strength within the retrieved Web pages is as weak as possible. Measuring association strength is based upon a novel confidence gain approach that relates the observed co-occurrence frequency for two sense descriptor candidates to an average co-occurrence frequency for pairs of arbitrary words. The proposed approach is fully unsupervised and takes into account the contemporary meanings of words, as reflected in texts from the Internet. Our results are evaluated using a list of ambiguous words commonly referred to in the literature.
In this study, a technique called semantic self-organization is used to scale up the subsymbolic approach by allowing a network to optimally allocate frame representations from a semantic dependency graph. The resulting architecture, INSOMNet, was trained on semantic representations of the newly-released LinGO Redwoods HPSG Treebank of anno-tated sentences from the VerbMobil project. The results show that INSOMNet is able to accurately represent the semantic dependencies while demonstrating expectations and defaults, coactivation of multiple interpretations, and robust process-ing of noisy input. The cognitive plausibility of the model is underscored by the collective modelling of four experiments from the visual worlds paradigm to show the model’s ability to adapt to context.
The annotation of the Prague Dependency Treebank (PDT) is conceived of as a multilayered scenario that comprises also dependency representations (tectogrammatical tree structures, TGTS’s) of the underlying structure of the sentences. TGTS’s capture three basic aspects of the underlying structure of sentences: (a) the dependency tree structure, (b) the kinds of dependency syntactic relations, and (c) the basic characteristics of the topic-focus articulation (TFA). Since the PDT is a large collection and the annotations on the deepest layer are to a large extent performed by several human annotators (based on an automatic preprocessing module), it is more than necessary to observe the consistence of annotators and the agreement among them. In the present paper, we summarize the results of the evaluation of parallel annotations of several samples taken from PDT and the measures accepted to improve the consistency of annotations.
Introduction The CorpusEye project (http://corp.hum.sdu.dk ) at the University of Denmark aims at designing and programming an internet based corpus search interface that (1) offers standardised search tools and a unified descriptive formalism across different corpus types and different languages, and (2) allows users to exploit grammatical information in annotated corpora in a user-friendly and menubased way. All corpora in CorpusEye have been annotated with VISL's Constraint Grammar based parsers, in the case of treebanks using an additional PSG module or equivalent (Bick 2003). At the time of writing, the material covers 8 languages and ca. 600 million words.
The claim made in this paper is that in a formal description of language, it is possible and useful to work with dependency-based underlying representations of sentences (tectogrammatical representations) meeting the condition of projectivity. The reasons for the inclusion of this condition into the definition of the tectogrammatical representations are both formally and empirically sound (Section 1). An analysis of the material offered by the Prague Dependency Treebank with annotations of the underlying syntactic structure of sentences (described in Section 2) has led to an interesting classification of non-projective constructions in Czech (Section 3). It documents that most (types of) constructions that appear to be non-projective in the surface shape of sentences can be described by means of projective trees. The realization of the surface word order (with the use of movement rules) is then relegated to the morphemic level, where the representation of the sentence has the shape of a string rather than a tree.
We discuss existing approaches to train LR parsers, which have been used for statistical resolution of structural ambiguity. These approaches are nonoptimal, in the sense that a collection of probability distributions cannot be obtained. In particular, some probability distributions expressible in terms of a context-free grammar cannot be expressed in terms of the LR parser constructed from that grammar, under the restrictions of the existing approaches to training of LR parsers. We present an alternative way of training that is provably optimal, and that allows all probability distributions expressible in the context-free grammar to be carried over to the LR parser. We also demonstrate empirically that this kind of training can be effectively applied on a large treebank.
Abstract This paper surveys work on applying the insights of lexicalized grammars to low‐level discourse, to show the value of positing an autonomous grammar for low‐level discourse in which words (or idiomatic phrases) are associated with discourse‐level predicate–argument structures or modification structures that convey their syntactic‐semantic meaning and scope. It starts by describing a lexicalized Tree Adjoining Grammar for discourse (D‐LTAG). It then reviews an initial experiment in parsing text automatically, using both a lexicalized TAG and D‐LTAG, and then touches upon issues involved in how lexico‐syntactic elements contribute to discourse semantics. The paper concludes with a brief description of the Penn Discourse TreeBank, a resource being developed for the study of discourse structure and semantics.
The modern dominant approach to translation is that which aims at intelligibility of the meaning and normality of the style in the translated text. Many translation theorists maintain that a translator should attempt to produce a target text which is clear and understandable in meaning, normal in style, and natural in language. To attain this goal, one is allowed and sometimes obliged, to make some adjustments such as expansion and reduction in the process of transfer according to the linguistic norms of the receptor language. However, in translating a highly sensitive religious text like the Qur'an, the accuracy of the meaning conveyed in the translated text is of primary importance, since it is the source text which is considered as the main criterion in evaluating the translation. Therefore, no translator is allowed to sacrifice accuracy of the meaning for the sake of intelligibility and naturalness in the translation, nor is it legitimate to make the meaning unintelligible or distorted by producing a target text which is awkward andunnatural. What on should do is to produce a translation natural in language, intelligible and accurate in meaning
We present discourse-level annotation of newspaper texts in German and English, as part of an ongoing project aimed at investigating information structure from a cross-linguistic perspective. Rather than annotating some specific notion of information structure, we propose a theory-neutral annotation of basic features at the levels of syntax, prosody and discourse, using treebank data as a starting point. Our discourse-level annotation scheme covers properties of discourse referents (e.g., semantic sort, delimitation, quantification, familiarity status) and anaphoric links (coreference and bridging). We illustrate what investigations this data serves and discuss some integration issues involved in combining different levels of stand-off annotations, created by using different tools.
The purpose of this study is to identify the properties of special-word, and to show the process of extracting special-words from a large corpus. A special-word corresponds to the notion of unknown words, which is a counterpart of the lexical database in Natural Language Process(NLP). Generally unknown words cause a lot of ambiguities and thus decline the accuracy of NLP systems. The special-word in this work includes various expressions about the events of the day or the fashions, abbreviated words and naturalized word. We came up with a semi-automatic procedure of constructing a special-word dictionary mainly based on the language-dependent heuristics. We, however, also feel that other statistical considerations including frequencies, and probability distributions may be required for unknown word extractions in a higher automatic fashion.
Function tags are a context-sensitive annotation applied to words and phrases of natural language text, marking their syntactic or semantic role within a larger utterance. As researchers improve results on various other problems in “pure” natural language processing (e.g part-of-speech tagging, parsing), those who work in the more “applied” NLP fields (e.g. question-answering, temporal analysis) are seeking more powerful sorts of linguistic annotation as input for their own systems. Hence, function tags. In the first part of the thesis, I present the problem of function tagging: why it is an interesting problem, who has worked on similar thing, and what exactly I intend to do. I briefly review the function tags of the Penn treebank, and explain the specific metrics by which I will evaluate my work. In the second part of the thesis, I introduce the many features that I will use to train a function tagging system, and then I present some systems that make use of them: one using feature trees, one using decision trees (briefly), and one using perceptron models. For each system, I give a brief historical perspective, an overview of where it has been used before and why I think it will be useful in this task. I will then try a number of feature combinations with interesting properties; and finally, present the best-performing tweaked-out version of that system. Finally, in the third part of the thesis, I bring them all together and discuss the advantages and disadvantages of each system in various situations. More interestingly, I will present an analysis of what features prove to be the most helpful for the different function tagging subtasks. Lastly, I will present a comparison to other systems performing related tasks, and speculate on some interesting future work.
The preceding articles by Piek Vossen (PV), Willy Martin (WM), and Marc van Campenoudt (MC) give a much more detailed account on their respective multilingual lexical database designs than the article by myself in this same journal (MJ). At the same time, they indicate some points of concern regarding the set-up of the SIM<it>u</it>LLDA system. Rather than responding directly to the points raised, this response elaborates on the two aspects of the SIM<it>u</it>LLDA system that seem to form the main sources of these issues: the status of the definitional attributes, and the practical usability of the system. The issues raised in the preceding articles will be explicitly addresses in the course of this elaboration. For even more details on these topics, see Janssen (2002).
The requirements of the depth and precision of annotation vary for different intended uses of the corpus but it has been commonly accepted nowadays that the standard annotations of surface structure are only the first steps in a more ambitious research program, aiming at a creation of advanced resources for most different systems of natural language processing and for testing and further enrichment of linguistic and computational theories. Among the several possible directions in which we believe the standard annotation systems should go (and in some cases already attempt to go) beyond the POS tagging or shallow syntactic annotations, the following four are characterized in the present contribution: (i) predicateargument representation of the underlying syntactic relations as basically corresponding to a rooted tree that can be univocally linearized, (ii) the inclusion of the information structure using very simple means (the left-to-right order of the nodes and three attribute values), (iii) relating this underlying structure (rendering the ”linguistic meaning,” i.e. the semantically relevant counterparts of the grammatical means of expression) to certain central aspects of referential semantics (reference assignment and coreferential relations), and (iv) handling of word sense disambiguation. The first three issues are documented in the present paper on the basis of our experience with the development of the structure and scenario of the Prague Dependency Treebank which provides for syntactico-semantic annotation of large text segments from the Czech National Corpus and which is based on a solid theoretical framework.
Hate crime laws are a highly controversial legal approach in society's response to intergroup violence. Argument acceptance, knowledge, and individual differences were examined in relationship to attitudes about these laws. These variables were also considered in terms of efforts to influence a peer's beliefs about hate crime laws. One‐hundred and sixty‐seven participants completed a measure of knowledge of human rights laws, Gough's Pr scale, the Selznick and Steinberg anti‐Semitism scale, and Cuellar's Machismo scale. Hate crime attitudes were measured on an affect rating scale and six statements reflecting arguments favoring and opposing hate crime laws. Peer influence was examined on Interpersonal Power Inventory (IPI). Results showed that while most participants endorsed positive attitudes about hate crime laws, men—and both women and men who endorsed machismo attitudes—were more likely to agree with media distortion and identity politics arguments opposing hate crime laws. The Pr and machismo scales predicted greater effort on the IPI to influence peer attitudes about hate crime laws, after controlling for demographic differences of the participants. These findings indicate that more explicitly biased individuals were more effortful in trying to change the attitudes of peers concerning the legitimacy of hate crime laws.
This paper investigates the usefulness of sentence-internal prosodic cues in syntactic parsing of transcribed speech. Intuitively, prosodic cues would seem to provide much the same information in speech as punctuation does in text, so we tried to incorporate them into our parser in much the same way as punctuation is. We compared the accuracy of a statistical parser on the LDC Switchboard treebank corpus of transcribed sentence-segmented speech using various combinations of punctuation and sentence-internal prosodic information (duration, pausing, and f0 cues).
OBJECTIVES: To examine affect and physiological stress in frail older adults in response to a voluntary nursing home relocation. DESIGN: Randomized, controlled trial. SETTING: Long-term care facility located within the greater Philadelphia, Pennsylvania, community. PARTICIPANTS: Seventy-seven nursing home residents, aged 65 and over. INTERVENTION: Experimental group residents were relocated to a newly built nursing home facility with a cluster design in the fall of 2001; control group residents were moved after study completion in the spring of 2002. MEASUREMENTS: Mini-Mental State Examination scores, Observed Affect Rating Scale scores, salivary cortisol, blood pressure, and pulse obtained 1 week before moving and 1 week and 4 weeks after moving. RESULTS: Relocated nursing home residents demonstrated significant differences in salivary cortisol and mood from a randomly selected group of residents that had not yet moved. Relocation resulted in significantly higher cortisol levels 1 week after the move (P=.005), followed by a significant decline in afternoon cortisol at 4 weeks after the move (P=.03). Moreover, relocated residents had significantly lower depression and anxiety symptoms and pulse rates than residents who had not yet moved. CONCLUSION: These findings have important implications for planning medical and social services for relocated elderly. Efforts should be made to prepare individuals for the initial stressors associated with relocation, but it also appears that the stress imposed by relocation is time limited and may begin to ease as early as 4 weeks postmove.
This paper describes the representation of Basque Multiword Lexical Units and the automatic processing of Multiword Expressions. After discussing and stating which kind of multiword expressions we consider to be processed at the current stage of the work, we present the representation schema of the corresponding lexical units in a general-purpose lexical database. Due to its expressive power, the schema can deal not only with fixed expressions but also with morphosyntactically flexible constructions. It also allows us to lemmatize word combinations as a unit and yet to parse the components individually if necessary. Moreover, we describe HABIL, a tool for the automatic processing of these expressions, and we give some evaluation results. This work must be placed in a general framework of written Basque processing tools, which currently ranges from the tokenization and segmentation of single words up to the syntactic tagging of general texts.
It has been noted that certain similarities can be detected between the work of the Bakhtin Circle and the work of Jürgen Habermas. While I do not deny that these sorts of similarities can be detected, I also argue that the insights of the Bakhtin Circle can be used to provide the basis of a critique of Habermas. My specific aim is to show how Habermas perpetuates a ‘stylistic’ approach to discourse theory. ‘Stylistics’, as conceived by the Bakhtin Circle, explores discourse through the dualism of an objectively defined set of linguistic norms that can be appropriated through the creative linguistic competence of individual speakers. On this understanding, individuals within their everyday environment choose elements from objective linguistic norms in order to make sense of their world. However, a stylistic approach to discourse implicitly denigrates the usefulness of everyday utterances because it suggests that, for language to gain normative meaning and understanding, it must always refer back to an objective linguistic system. By using the Bakhtin Circle’s theoretical insights, I flag up several problems that emerge from Habermas’s particular standpoint on stylistics.
Schema matching is prerequisite to an automated transformation of XML documents. Because previous works about schema matching compute all semantically-possible matchings, they produce many-to-many matching relationships. Such imprecise matchings are inappropriate for an automated transformation of XML documents. This paper presents an efficient schema matching algorithm that computes precise one-to-one matchings between two schemas. The proposed algorithm consists of two steps: preliminary matching relationships between leaf nodes in the two schemas are computed and one-to-one matchings are finally extracted based on a proposed path similarity. Specifically, for a sophisticated schema matching, the proposed algorithm is based on a domain ontology as well as a lexical database that includes abbreviations and synonyms. Experimental results with real schemas from an e-commerce field show that the proposed method is superior to previous works, resulting in an accuracy of 97% in average.
The majority of electronic data today is in textual form.Financial data such as articles in the Wall Street Journal are written as texts.These electronic documents contain a wealth of information but require human interpretation.For financial analysis, rapid up-to-date information is critical.Most software tools currently require data which are better structured than text (such as data in relational databases).Thus, our research goal is to build a system, "FIRST" (Flexible Information extRaction SysTem), that will extract data from financial articles and store the output in an explicit format.FIRST uses natural language processing techniques and resources such as the lexical database WordNet and collocation information to extract information.We hope to be able to extract data such as an organization's name, its profit/loss status, and sales status, from financial articles to input into a database.The data will come from international corporate reports which appear in the Wall Street Journal.
The classification algorithm based on SVM (support vector machine) attracts more attention from researchers due to its perfect theoretical properties and good empirical results. Compared with other classification algorithms, structural risk minimizations based SVM achieve high generalization performance with small number of samples. The text chunking, as a preprocessing step for parsing, is to divide text into syntactically related non-overlapping groups of words (chunks), reducing the complexity of the full parsing. In this paper, we treat Chinese text chunking as a classification problem, and apply SVM to solve it. The chunking experiments were carried out on the HIT Chinese Treebank corpus. Experimental results show that it is an effective approach, achieving an F score of 88.67%, especially for a small number of Chinese labeled samples.
Predicting the location of phrase breaks within an utterance is an important task in text-to-speech synthesis, and can be done with reasonable accuracy using part-of-speech (POS) tags as features. However, it seems unlikely that the 40 or more different tags used by most taggers all contribute to this task, and in fact many may contribute noise. In this paper, we present an algorithm for reducing the standard Penn Treebank POS tag set for use in predicting phrase breaks. Using the best first search approach, the algorithm considers possible groupings of tags, searching the groupings that yield the highest overall performance. The reduced tag sets were evaluated by an n-gram model trained on POS sequences along with their associated juncture (break/non-break), the reduced tag set raised the model's performance on junctures correct from 90.38% to 92.43%, and reduced insertions from 2.89% to 1.83%.
Abstract This study attempted to demonstrate an elevated disgust sensitivity in bulimia nervosa. Eleven bulimic patients and 12 control subjects underwent a functional magnetic resonance imaging (fMRI) study in which they were presented with alternating blocks of 40 disgust‐inducing, 40 fear‐inducing and 40 affectively neutral scenes. Each scene was shown for 1.5 s. After completion of all blocks, affective ratings were then determined. The viewing of the disgusting pictures, which had been rated as highly repulsive by the bulimic females, was associated with an activation of the left amygdala and the occipito‐temporal visual cortex. The subjective and brain‐physiological responses did not differ from those of the healthy control subjects. This held true for the fear‐inducing scenes as well. Thus, bulimic patients are not characterized by an increased global disgust sensitivity and they do not show any indication of an altered central processing of generally disgust and fear‐inducing visual stimuli. Copyright © 2004 John Wiley & Sons, Ltd and Eating Disorders Association.
The basic prerequisite for using any language is the willingness of the speaker to follow the rules of the game. Socially defined norms of language use then tend to set the limits within which one can express oneself using this language. Whether these norms set the speaker free or whether they act as constraints in a free expression of Self, is a question that will be raised in this article. Using examples from Hindi, the paper highlights the role of such norms of language use in perpetuating gender stereotypes. Gender stereotypes get constructed as part of a broader process of social differentiation but the site of this construction is to a large extent the normal everyday discourse. A normal classroom discussion amongst university students in New Delhi thus shows how deep rooted such stereotypes are and how effectively they get perpetuated through language and linguistic norms in Indian society. The basic premise in this paper is that meanings are context-specific, they are not fixed and they get created in discourse. But since language use is one thread in social fabric, it serves as an instrument to construct and perpetuate gender stereotypes. The paper is more of an essay on issues that became obvious about gender stereotypes during two classroom discussions. It should not therefore be taken as a study into the deeper aspects of gender representation in Hindi.
We describe the development of a Dutch memory-based shallow parser. The availability of large treebanks for Dutch, such as the one provided by the Spoken Dutch Corpus, allows memory-based learners to be trained on examples of shallow parsing taken from the treebank, and act as a shallow parser after training. An overview is given of a modular memory-based learning approach to shallow parsing, composed of a part-of-speech tagger-chunker and two grammatical relation finders, which has originally been developed for English. This approach is applied to the syntactically annotated part of the Spoken Dutch Corpus to construct a Dutch shallow parser. From the generalisation scores of the parser we conclude that existing memory-based parsing approaches can be applied to spoken Dutch successfully, but that there is room for improvement in the tagger-chunker
Abstract We present a computational language learning algorithm that can induce massively probabilistic grammars from treebank data. The paper is based on chapter 6 in my forthcoming thesis “Discontinuous Grammar: A dependency-based model of human parsing and language learning.” We start in section 1 by introducing the prerequisites from probability theory and statistics that are needed in the rest of the paper. In section 2, we define weighted grammars and massively probabilistic grammars, and discuss their relationship to standardly used grammars. In section 3, we address the problem of estimating probability distributions for hierarchically structured categorical data, and present an estimation algorithm that selects a hierarchical partition model by means of local search. We also describe a simulation study that shows that the algorithm performs well unless the distribution that generated the data is highly symmetric. Finally, in section 4, we outline how the algorithm can be used to learn massively probabilistic dependency grammars, exemplified by the task of learning the probabilities of complement structures.
Finding definitions in huge text collections is a challenging problem, not only because of the many ways in which defini-tions can be conveyed in natural language texts but also be-cause the definiendum (i.e., the thing to be defined) has not, on its own, enough discriminative power to allow selection of definition-bearing passages from the collection. We have de-veloped a method that uses already available external sources to gather knowledge about the “definiendum ” before trying to define it using the given text collection. This knowledge consists of lists of relevant secondary terms that frequently co-occur with the definiendum in definition-bearing passages or “definiens”. External sources used to gather secondary terms are an on-line enyclopedia, a lexical database and the Web. These secondary terms together with the definiendum are used to select passages from the text collection perform-ing information retrieval. Further linguistic analysis is car-ried out on each passage to extract definition strings from the passages using a number of criteria including the presence of main and secondary terms or definition patterns.
1. Preface 2. Words, numbers and all that: The lexicon in sentence understanding (by Stevenson, Suzanne) 3. The lexicon in Optimality Theory (by Bresnan, Joan) 4. Optimality-theoretic Lexical Functional Grammar (by Johnson, Mark) 5. The lexicon and the laundromat (by Fodor, Jerry) 6. Semantics in the spin cycle: Competence and performance criteria for the creation of lexical entries (by Weinberg, Amy) 7. Connectionist and symbolist sentence processing (by Steedman, Mark) 8. A computational model of the grammatical aspects of word recognition as supertagging (by Kim, Albert E.) 9. Incrementality and lexicalism: A treebank study (by Lombardo, Vincenzo) 10. Modular architectures and statistical mechanisms: The case from lexical category disambiguation (by Crocker, Matthew) 11. Encoding and storage in working memory during sentence comprehension (by Stowe, Laurie A.) 12. The time course of information integration in sentence processing (by Spivey, Michael J.) 13. The lexical source of unexpressed participants and their role in sentence and discourse understanding (by Mauner, Gail) 14. Reduced relatives judged hard require constraint-based analyses (by Filip, Hana) 15. Predicting thematic role assignments in context (by Altmann, Gerry T.M.) 16. Lexical semantics as a basis for argument structure frequency biases (by Argamann, Vera) 17. Verb sense and verb subcategorization probabilities (by Roland, Doug) 18. Author index 19. Item index
Concept mapping is a knowledge elicitation technique which stimulates learners to articulate and synthesize their actual states of knowledge during the learning process. Several approaches have been proposed for automating the assessment procedure of learners' concept maps based on an expert's map as a reference point. However, these approaches do not handle cases where learners have misspelled a concept or they have used a synonym or a concept related to the appropriate one. In this paper we present an alternative approach in which the process of the error identification is performed through the use of an expert map and of WordNet which is an electronic lexical database containing semantic relationships between words. This way we handle cases such as misspelled concepts, synonyms and related concepts. After error detection WordNet is also employed for providing the learner with appropriate feedback based on the identified errors, with the intention of helping the learner to correct them.
Biomimetic design uses ideas from biological phenomena as inspiration in design. To support biomimetic design, biological analogies are identified by finding instances of functional keywords that describe the engineering problem in biological knowledge in natural-language format. Challenges in using this approach include the identification of keywords, and the quantity and quality of results found. WordNet, a lexical database, is used as a language framework to systematically generate alternative keywords to find matches and analyze the results of searches. Troponyms from WordNet were found to provide better and more plentiful keywords than did synonyms. Due to the potentially large number of matches to keywords, matches are analyzed to facilitate extraction of dominant biological phenomena associated with keywords. This analysis found that words that frequently collocated with keywords tend to be objects of the keyword verb or agents that carry out the actions of the keyword. Furthermore, nouns that are inanimate, e.g., substances, tend to be objects, and nouns that are animate e.g., animals, organs, tend to be agents. Distinguishing frequently collocated words and their relationships to keywords can be used to facilitate identification of biological analogies in natural-language format to support design.Copyright © 2004 by ASME