Minimally supervised question classification and answering based on WordNet and Wikipedia
|Minimally supervised question classification and answering based on WordNet and Wikipedia|
|Author(s)||Chang J., Yen T.-H., Tsai R.T.-H.|
|Published in||Proceedings of the 21st Conference on Computational Linguistics and Speech Processing, ROCLING 2009|
|Keyword(s)||Question answering, Question classification, Semantic category, Wikipedia, WordNet (Extra: Question Answering, Question classification, Semantic category, Wikipedia, Wordnet, Classification (of information), Computational linguistics, Natural language processing systems, Ontology, Semantics, Speech processing, Websites)|
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Minimally supervised question classification and answering based on WordNet and Wikipedia is a 2009 conference paper written in English by Chang J., Yen T.-H., Tsai R.T.-H. and published in Proceedings of the 21st Conference on Computational Linguistics and Speech Processing, ROCLING 2009.
In this paper, we introduce an automatic method for classifying a given question using broad semantic categories in an existing lexical database (i.e., WordNet) as the class tagset. For this, we also constructed a large scale entity supersense database that contains over 1.5 million entities to the 25 WordNet lexicographer's files (supersenses) from titles of Wikipedia entry. To show the usefulness of our work, we implement a simple redundancy-based system that takes the advantage of the large scale semantic database to perform question classification and named entity classification for open domain question answering. Experimental results show that the proposed method outperform the baseline of not using question classification.
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