997 resultados para Computational Lexical Semantics
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The article belongs to the field of lexical semantics studies, associated with describing the Russian linguistic world-image. The work focuses on the universal situation of purchase and sale as reflected in the Russian language.
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The thesis belongs to the field of lexical semantics studies, associated with describing the Russian linguistic world-image. The research focuses on the universal situation of purchase and sale as reflected in the Russian lexical standard and sub-standard. The work deals also with subjects related to the sphere of social linguistics: the social stratification of the language, the structure of sub-standard, etc. The thesis is a contribution to the description of the Russian linguistic world-image as well as to the further elaboration of the conceptional analysis method. The results are applicable in teaching Russian as a foreign language, particularly in lexis and Russian culture and mentality studies.
A guerra EUA x Iraque no discurso jornalístico: análise léxico-semântica das unidades de denominação
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Pós-graduação em Linguística e Língua Portuguesa - FCLAR
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Reading and listening involve complex psychological processes that recruit many brain areas. The anatomy of processing English words has been studied by a variety of imaging methods. Although there is widespread agreement on the general anatomical areas involved in comprehending words, there are still disputes about the computations that go on in these areas. Examination of the time relations (circuitry) among these anatomical areas can aid in understanding their computations. In this paper, we concentrate on tasks that involve obtaining the meaning of a word in isolation or in relation to a sentence. Our current data support a finding in the literature that frontal semantic areas are active well before posterior areas. We use the subject’s attention to amplify relevant brain areas involved either in semantic classification or in judging the relation of the word to a sentence to test the hypothesis that frontal areas are concerned with lexical semantics and posterior areas are more involved in comprehension of propositions that involve several words.
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This paper addresses the problem of the automatic recognition and classification of temporal expressions and events in human language. Efficacy in these tasks is crucial if the broader task of temporal information processing is to be successfully performed. We analyze whether the application of semantic knowledge to these tasks improves the performance of current approaches. We therefore present and evaluate a data-driven approach as part of a system: TIPSem. Our approach uses lexical semantics and semantic roles as additional information to extend classical approaches which are principally based on morphosyntax. The results obtained for English show that semantic knowledge aids in temporal expression and event recognition, achieving an error reduction of 59% and 21%, while in classification the contribution is limited. From the analysis of the results it may be concluded that the application of semantic knowledge leads to more general models and aids in the recognition of temporal entities that are ambiguous at shallower language analysis levels. We also discovered that lexical semantics and semantic roles have complementary advantages, and that it is useful to combine them. Finally, we carried out the same analysis for Spanish. The results obtained show comparable advantages. This supports the hypothesis that applying the proposed semantic knowledge may be useful for different languages.
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The paper relates about our ongoing work on the creation of a corpus of Bulgarian and Ukrainian parallel texts. We discuss some differences in the approaches and the interpretation of some concepts, as well as various problems associated with the construction of our corpus, in particular the occasional ‘nonparallelism’ of original and translated texts. We give examples of the application of the parallel corpus for the study of lexical semantics and note the outstanding role of the corpus in the lexicographic description of Ukrainian and Bulgarian translation equivalents. We draw attention to the importance of creating parallel corpora as objects of national as well as global cultural heritage.
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This PhD project aims to study paraphrasing, initially understood as the different ways in which the same content is expressed linguistically. We will go into that concept in depth trying to define and delimit its scope more accurately. In that sense, we also aim to discover which kind of structures and phenomena it covers. Although there exist some paraphrasing typologies, the great majority of them only apply to English, and focus on lexical and syntactic transformations. Our intention is to go further into this subject and propose a paraphrasing typology for Spanish and Catalan combining lexical, syntactic, semantic and pragmatic knowledge. We apply a bottom-up methodology trying to collect evidence of this phenomenon from the data. For this purpose, we are initially using the Spanish Wikipedia as our corpus. The internal structure of this encyclopedia makes it a good resource for extracting paraphrasing examples for our investigation. This empirical approach will be complemented with the use of linguistic knowledge, and by comparing and contrasting our results to previously proposed paraphrasing typologies in order to enlarge the possible paraphrasing forms found in our corpus. The fact that the same content can be expressed in many different ways presents a major challenge for Natural Language Processing (NLP) applications. Thus, research on paraphrasing has recently been attracting increasing attention in the fields of NLP and Computational Linguistics. The results obtained in this investigation would be of great interest in many of these applications.
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Acquiring lexical information is a complex problem, typically approached by relying on a number of contexts to contribute information for classification. One of the first issues to address in this domain is the determination of such contexts. The work presented here proposes the use of automatically obtained FORMAL role descriptors as features used to draw nouns from the same lexical semantic class together in an unsupervised clustering task. We have dealt with three lexical semantic classes (HUMAN, LOCATION and EVENT) in English. The results obtained show that it is possible to discriminate between elements from different lexical semantic classes using only FORMAL role information, hence validating our initial hypothesis. Also, iterating our method accurately accounts for fine-grained distinctions within lexical classes, namely distinctions involving ambiguous expressions. Moreover, a filtering and bootstrapping strategy employed in extracting FORMAL role descriptors proved to minimize effects of sparse data and noise in our task.
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Lexical Resources are a critical component for Natural Language Processing applications. However, the high cost of comparing and merging different resources has been a bottleneck to have richer resources with a broad range of potential uses for a significant number of languages.With the objective of reducing cost byeliminating human intervention, we present a new method for automating the merging of resources,with special emphasis in what we call the mapping step. This mapping step, which converts the resources into a common format that allows latter the merging, is usually performed with huge manual effort and thus makes the whole process very costly. Thus, we propose a method to perform this mapping fully automatically. To test our method, we have addressed the merging of two verb subcategorization frame lexica for Spanish, The resultsachieved, that almost replicate human work, demonstrate the feasibility of the approach.
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Lexical Resources are a critical component for Natural Language Processing applications. However, the high cost of comparing and merging different resources has been a bottleneck to obtain richer resources and a broader range of potential uses for a significant number of languages. With the objective of reducing cost by eliminating human intervention, we present a new method towards the automatic merging of resources. This method includes both, the automatic mapping of resources involved to a common format and merging them, once in this format. This paper presents how we have addressed the merging of two verb subcategorization frame lexica for Spanish, but our method will be extended to cover other types of Lexical Resources. The achieved results, that almost replicate human work, demonstrate the feasibility of the approach.
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This work discusses a proposition for organizing the lexical items from the conceptual domain labeled THE EMBROIDERY INDUSTRY OF IBITINGA in terms of a natural ontology. It also aims to establish the alignment between this ontology and the bases WordNet.Pr and WordNet.Br. © 2009 IEEE.