Semantic taxonomy induction from heterogenous evidence

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Semantic taxonomy induction from heterogenous evidence (2006)

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by Rion Snow

Venue:	In Proceedings of COLING/ACL 2006
Citations:	216 - 1 self

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@INPROCEEDINGS{Snow06semantictaxonomy,
    author = {Rion Snow},
    title = {Semantic taxonomy induction from heterogenous evidence},
    booktitle = {In Proceedings of COLING/ACL 2006},
    year = {2006},
    pages = {801--808}
}

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Abstract

We propose a novel algorithm for inducing semantic taxonomies. Previous algorithms for taxonomy induction have typically focused on independent classifiers for discovering new single relationships based on hand-constructed or automatically discovered textual patterns. By contrast, our algorithm flexibly incorporates evidence from multiple classifiers over heterogenous relationships to optimize the entire structure of the taxonomy, using knowledge of a word’s coordinate terms to help in determining its hypernyms, and vice versa. We apply our algorithm on the problem of sense-disambiguated noun hyponym acquisition, where we combine the predictions of hypernym and coordinate term classifiers with the knowledge in a preexisting semantic taxonomy (WordNet 2.1). We add 10, 000 novel synsets to WordNet 2.1 at 84 % precision, a relative error reduction of 70 % over a non-joint algorithm using the same component classifiers. Finally, we show that a taxonomy built using our algorithm shows a 23 % relative F-score improvement over WordNet 2.1 on an independent testset of hypernym pairs. 1

Keyphrases

semantic taxonomy induction heterogenous evidence component classifier vice versa textual pattern previous algorithm novel synset taxonomy induction non-joint algorithm new single relationship coordinate term classifier hypernym pair independent testset multiple classifier semantic taxonomy novel algorithm word coordinate term independent classifier preexisting semantic taxonomy heterogenous relationship relative error reduction sense-disambiguated noun hyponym acquisition entire structure

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