Rol semantikoen etiketatze automatikoa

The main task of semantic role labeling (SRL), sometimes also called shallow semantic parsing , is to detect the semantic relations hold among the predicate of a sentence and its associated participants and properties and the classification into their spec ific roles . Perforrrung sentence-level semantic analysis can help determine who did whar to whom, where, when, and how within an event . The predicate of a clause (typically a verb) establishes what took place , and other sentence constituents express the participants in the event (such as who and where), as well as further event properties (such as when and how). The information provided by semantic roles is crucial in order to process texts automatically, and in addition to the applications in Natural Language Processing (NLP), semantic roles can help improve Internet search engines, question answering and translation systems. Nowadays , roles are on the edge regarding information extraction and social network research tasks.; Rol semantikoen etiketatze automatikoa (SRL), azaleko anali si semantikoa ere deitua , hi zkuntzalaritza konputazionalaren ikerlerro garrantzitsua da eta bertan, zehatz finkatu nahj dira testu bateko gertakarietan, ekjntza eta honetan parte hartzen dutenen arteko erlazio semantikoak edo rolak; berez, nork, nori, zer egin zion, non eta noiz gertatu den jakin nahi da. Rolek eskaintzen duten informazioak berebiziko garrantzia dauka testuak automatikoki prozesatu eta ulertzeko bidean. Ataza hau zeresan handia ematen ari da hizkuntzaren prozesamenduan ez ezik, besteak beste, Interneteko bilatzaileetan , itzulpen automatikoko eta galdera-erantzun sistemetan, sare sozialen azterketa automatikoan, eta dokumentuen informazio erauzketan

J. D. B. ミラー「太平洋経済共同体 : その問題と可能性」

The main task of semantic role labeling (SRL), sometimes also called shallow semantic parsing , is to detect the semantic relations hold among the predicate of a sentence and its associated participants and properties and the classification into their spec ific roles . Perforrrung sentence-level semantic analysis can help determine who did whar to whom, where, when, and how within an event . The predicate of a clause (typically a verb) establishes what took place , and other sentence constituents express the participants in the event (such as who and where), as well as further event properties (such as when and how). The information provided by semantic roles is crucial in order to process texts automatically, and in addition to the applications in Natural Language Processing (NLP), semantic roles can help improve Internet search engines, question answering and translation systems. Nowadays , roles are on the edge regarding information extraction and social network research tasks.; Rol semantikoen etiketatze automatikoa (SRL), azaleko anali si semantikoa ere deitua , hi zkuntzalaritza konputazionalaren ikerlerro garrantzitsua da eta bertan, zehatz finkatu nahj dira testu bateko gertakarietan, ekjntza eta honetan parte hartzen dutenen arteko erlazio semantikoak edo rolak; berez, nork, nori, zer egin zion, non eta noiz gertatu den jakin nahi da. Rolek eskaintzen duten informazioak berebiziko garrantzia dauka testuak automatikoki prozesatu eta ulertzeko bidean. Ataza hau zeresan handia ematen ari da hizkuntzaren prozesamenduan ez ezik, besteak beste, Interneteko bilatzaileetan , itzulpen automatikoko eta galdera-erantzun sistemetan, sare sozialen azterketa automatikoan, eta dokumentuen informazio erauzketan

Rol semantikoen etiketatze automatikoa : rol multzoak eta hautapen murriztapenak

158 p. : graf.[Eus]Tesi honetan, Rolen Sailkatze Automatikoan (RSA) aski ezagunak diren bi arazo izan ditugu aztergai: (1) Rol multzo ezberdinen egokitasuna praktikan, eta (2) RSArako sistemek darabiltzaten ezaugarri lexikalen eragin mugatua eta pairatzen duten sakabanaketa. Lehen puntuari dagokionez, gaur egun gure arloan gehien erabiltzen diren PropBank eta VerbNeteko rol multzoen azterketa konparatibo sakona aurkeztuko dugu, rol multzo bakoitzarekin entrenatutako sailkatzaileen errendimendua, sendotasuna, eta orokortzeko gaitasuna,esperimentazio ingurune eta domeinu ezberdinetan neurtuz. Bigarren puntuari dagokionez, ezaugarri lexikoek planteatzen dituzten arazoak aztertuko ditugu eta, WordNet eta antzekotasun distribuzionaleko neurriekin sortutako hautapen murriztapenak erabiliz, arazo horien eragina modu esanguratsuan leunduko dugu. In-vitro egindako esperimentuekin, hautapen murriztapen horiek lexikotik eratorritako ezaugarriek baino sailkatze ahalmen handiagoa dutela ikusiko dugu. Azkenik, hautapen murriztapenetatik erauzitako ezaugarriak baliatuz, artearen egoeran dagoen RSA sistema baten errendimendua hobetuko dugu (domeinu barnean eta domeinuz kanpo).[Eng]This thesis focuses on two well-known open issues in Semantic Role Classi fication (SRC) research: (1) the suitability of diferent role inventories in practice, and (2) the limited in uence and sparseness of lexical features. About the former, we present an empirical comparative study on the use of PropBank vs. VerbNet roles, the two most widely used role inventories, testing the performance diferences for unseen verbs and the robustness for new corpus domains. About the latter, we test the use of automatically learnt selectional preferences as a complement to lexical features, proposing both WordNet-based and distributional similarity based models. We show that all our selectional preference models improve over lexical features in in-vitro experiments, and that the models are complementary. Finally, we show that incorporating features based on selectional preferences, the overall performance of an state-of-the-art SRC system improves both in in-domain and out-of-domain corpora.Lan hau EHUko ikerketa beka baten laguntzaz egin da (2005-2009

First approach toward Semantic Role Labeling for Basque

Abstract In this paper, we present the first Semantic Role Labeling system developed for Basque. The system is implemented using machine learning techniques and trained with the Reference Corpus for the Processing of Basque (EPEC). In our experiments the classifier that offers the best results is based on Support Vector Machines. Our system achieves 84.30 F1 score in identifying the PropBank semantic role for a given constituent and 82.90 F1 score in identifying the VerbNet role. Our study establishes a baseline for Basque SRL. Although there are no directly comparable systems for English we can state that the results we have achieved are quite good. In addition, we have performed a Leave-One-Out feature selection procedure in order to establish which features are the worthiest regarding argument classification. This will help smooth the way for future stages of Basque SRL and will help draw some of the guidelines of our research

Cross-lingual event-mining using wordnet as a shared knowledge interface

We describe a concept-based event-mining system that maximizes information extracted from text and is not restricted to predefined knowledge templates. Such a system needs to handle a wide range of expressions while being able to extract precise semantic relations. The system uses simple patterns of linguistic and ontological constraints that are applied to a uniform representation of the text. It uses a generic ontology based on DOLCE and wordnets in different languages to extract events from text in these languages in an interoperable way. The system performs unsupervised domain-independent event-mining with promising results. Error-analysis showed that the semantic model and the mapping of text to concepts through wordsense-disambiguation (WSD) are not the main cause of the errors but the complexity of the grammatical structures and the quality of parsing. Using the same semantic model and their cross-wordnet links, our English event-mining patterns were transferred to Dutch in less than a day’s work. The platform was tested on the environment domain but can be applied to any other domain.