Study on assistant concept acquisition in domain ontology construction for Chinese texts

Concept acquisition is an important part of domain ontology construction, and how to accomplish assistant concept acquisition becomes a research focus. In this paper, a character-based CRF model is adopted to obtain the set of candidate terms, and we propose an active learning algorithm to select a...

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Vydáno v:	2011 7th International Conference on Natural Language Processing and Knowledge Engineering (NLPKE) s. 177 - 182
Hlavní autoři:	Guiping Zhang, Xiaoying Zhang, Peiyan Wang, Dongfeng Cai
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 01.11.2011
Témata:	active learning algorithm concept acquisition CRF model domain ontology Educational institutions Feature extraction Hafnium Manuals Ontologies
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Popis
Shrnutí:	Concept acquisition is an important part of domain ontology construction, and how to accomplish assistant concept acquisition becomes a research focus. In this paper, a character-based CRF model is adopted to obtain the set of candidate terms, and we propose an active learning algorithm to select a concept from the set of candidate terms for the user and use the stochastic gradient descent algorithm for training the weight of concepts. The experiment results show that this algorithm can effectively assist user acquire domain concepts, when the set of correct terms identified by the CRF model is used as candidate concepts, the value of MAP reaches 0.9335.
DOI:	10.1109/NLPKE.2011.6138190