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• W2566468052 abstract "Modern information systems are changing the idea of “data processing” to the idea of “conceptprocessing”, meaning that instead of processing words, such systems process semantic conceptswhich carry meaning and share contexts with other concepts. Ontology is commonly used as astructure that captures the knowledge about a certain area via providing concepts and relationsbetween them. Traditionally, concept hierarchies have been built manually by knowledge engineers or domainexperts. However, the manual construction of a concept hierarchy suffers from several limitationssuch as its coverage and the enormous costs of extension and maintenance. Furthermore, keepingup with a hand-crafted concept hierarchy along with the evolution of domain knowledge is anoverwhelming task, being necessary to build concept hierarchies automatically. The (semi-)automatic support in ontology development is usually referred to as ontology learning.The ontology learning from texts is usually divided in steps, going from concepts identification,passing through hierarchy and non-hierarchy relations detection and, seldom, axiom extraction. Itis reasonable to say that among these steps the current frontier is in the establishment of concepthierarchies, since this is the backbone of ontologies and, therefore, a good concept hierarchy isalready a valuable resource for many ontology applications. A concept hierarchy is represented with a tree-structured form with specialization/generalizationrelations between concepts, in which lower-level concepts are more specific while higher-level aremore general. The automatic construction of concept hierarchies from texts is a complex task andsince the 1980 decade a large number of works have been proposing approaches to better extractrelations between concepts. These different proposals have never been contrasted against each otheron the same set of data and across different languages. Such comparison is important to see whetherthey are complementary or incremental, also we can see whether they present different tendenciestowards recall and precision, i.e., some can be very precise but with very low recall and others canachieve better recall but low precision. Another aspect concerns to the variation of results for different languages. This thesis evaluatesthese different methods on the basis of hierarchy metrics such as density and depth, and evaluationmetrics such as Recall and Precision. The evaluation is performed over the same corpora, whichconsist of English and Portuguese parallel and comparable texts. Both automatic and manualevaluations are presented. The output of seven methods are evaluated automatically and the outputof four methods are evaluated manually. Results shed light over the comprehensive set of methodsthat are the state of the art according to the literature in the area." @default.
• W2566468052 created "2017-01-06" @default.
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• W2566468052 date "2015-09-28" @default.
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• W2566468052 title "Evaluation of methods for taxonomic relation extraction from text" @default.
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