Dependency tree kernels for relation extraction

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Dependency tree kernels for relation extraction (2004)

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by Aron Culotta

Venue:	In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04
Citations:	262 - 2 self

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@INPROCEEDINGS{Culotta04dependencytree,
    author = {Aron Culotta},
    title = {Dependency tree kernels for relation extraction},
    booktitle = {In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04},
    year = {2004},
    pages = {423--429}
}

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Abstract

We extend previous work on tree kernels to estimate the similarity between the dependency trees of sentences. Using this kernel within a Support Vector Machine, we detect and classify relations between entities in the Automatic Content Extraction (ACE) corpus of news articles. We examine the utility of different features such as Wordnet hypernyms, parts of speech, and entity types, and find that the dependency tree kernel achieves a 20 % F1 improvement over a “bag-of-words ” kernel. 1

Keyphrases

dependency tree kernel relation extraction support vector machine wordnet hypernym bag-of-words kernel automatic content extraction previous work entity type tree kernel dependency tree news article different feature f1 improvement

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