Dependency tree kernels for relation extraction
Published 1 January 2004Open access
Aron Culotta, Jeffrey Sorensen
Citations825
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TL;DR
This work extends previous work on tree kernels to estimate the similarity between the dependency trees of sentences, and uses this kernel within a Support Vector Machine to detect and classify relations between entities in the Automatic Content Extraction (ACE) corpus of news articles.
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.
Keywords
Computer Science
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