WikiSense: Supersense tagging of Wikipedia named entities based WordNet
|WikiSense: Supersense tagging of Wikipedia named entities based WordNet|
|Author(s)||Chang J., Tsai R.T.-H., Chang J.S.|
|Published in||PACLIC 23 - Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation|
|Keyword(s)||Semantic category, Wikipedia, Word sense disambiguation, WordNet (Extra: Classification models, Named entities, Natural languages, Semantic category, Sense-tagging, Training data, Wikipedia, Word Sense Disambiguation, Wordnet, Classification (of information), Natural language processing systems, Semantics, Websites, Ontology)|
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WikiSense: Supersense tagging of Wikipedia named entities based WordNet is a 2009 journal article written in English by Chang J., Tsai R.T.-H., Chang J.S. and published in PACLIC 23 - Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation.
In this paper, we introduce a minimally supervised method for learning to classify named-entity titles in a given encyclopedia into broad semantic categories in an existing ontology. Our main idea involves using overlapping entries in the encyclopedia and ontology and a small set of 30 handed tagged parenthetic explanations to automatically generate the training data. The proposed method involves automatically recognizing whether a title is a named entity, automatically generating two sets of training data, and automatically building a classification model for training a classification model based on textual and non-textual features. We present WikiSense, an implementation of the proposed method for extending the named entity coverage of WordNet by sense tagging Wikipedia titles. Experimental results show WikiSense achieves accuracy of over 95% and near 80% applicability for all NE titles in Wikipedia. WikiSense cleanly produces over 1.2 million of NEs tagged with broad categories, based on the lexicographers' files of WordNet, effectively extending WordNet to form a very large scale semantic category, a potentially useful resource for many natural language related tasks. © 2009 by Joseph Chang, Richard Tzong-Han Tsai, and Jason S. Chang.
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