Document (#36848)

Author
Bounhas, I.
Elayeb, B.
Evrard, F.
Slimani, Y.
Title
Organizing contextual knowledge for Arabic text disambiguation and terminology extraction
Source
Knowledge organization. 38(2011) no.6, S.473-490
Year
2011
Abstract
Ontologies have an important role in knowledge organization and information retrieval. Domain ontologies are composed of concepts represented by domain relevant terms. Existing approaches of ontology construction make use of statistical and linguistic information to extract domain relevant terms. The quality and the quantity of this information influence the accuracy of terminology extraction approaches and other steps in knowledge extraction and information retrieval. This paper proposes an approach for handling domain relevant terms from Arabic non-diacriticised semi-structured corpora. In input, the structure of documents is exploited to organize knowledge in a contextual graph, which is exploited to extract relevant terms. This network contains simple and compound nouns handled by a morphosyntactic shallow parser. The noun phrases are evaluated in terms of termhood and unithood by means of possibilistic measures. We apply a qualitative approach, which weighs terms according to their positions in the structure of the document. In output, the extracted knowledge is organized as network modeling dependencies between terms, which can be exploited to infer semantic relations. We test our approach on three specific domain corpora. The goal of this evaluation is to check if our model for organizing and exploiting contextual knowledge will improve the accuracy of extraction of simple and compound nouns. We also investigate the role of compound nouns in improving information retrieval results.
Footnote
Vgl.: http://www.ergon-verlag.de/isko_ko/downloads/ko_38_2011_6_b.pdf.

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  5. Li, J.; Zhang, Z.; Li, X.; Chen, H.: Kernel-based learning for biomedical relation extraction (2008) 0.24
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