We extend previous work on tree kernels to estimate the similarity between the dependency trees of sen- tences. 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 depen- dency tree kernel achieves a 20% F1 improvement over a “bag-of-words” kernel.

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