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Semantic relation extraction with kernels over typed dependency trees

: Reichartz, F.; Korte, H.; Paaß, G.

Preprint urn:nbn:de:0011-n-1385310 (375 KByte PDF)
MD5 Fingerprint: 330759c424bfa8f8c44994f5be1cdf5d
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Erstellt am: 18.8.2010

Rao, B. ; Association for Computing Machinery -ACM-, Special Interest Group on Knowledge Discovery and Data Mining -SIGKDD-; Association for Computing Machinery -ACM-, Special Interest Group on Management of Data -SIGMOD-:
KDD 2010, 16th ACM SIGKDD international conference on Knowledge discovery and data mining. Proceedings : Washington, DC, USA, July 25-28, 2010
New York: ACM, 2010
ISBN: 978-1-4503-0055-1
International Conference on Knowledge Discovery and Data Mining (KDD) <16, 2010, Washington/DC>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()
information extraction; relation extraction; parse tree

An important step for understanding the semantic content of text is the extraction of semantic relations between entities in natural language documents. Automatic extraction techniques have to be able to identify different versions of the same relation which usually may be expressed in a great variety of ways. Therefore these techniques benefit from taking into account many syntactic and semantic features, especially parse trees generated by automatic sentence parsers. Typed dependency parse trees are edge and node labeled parse trees whose labels and topology contains valuable semantic clues. This information can be exploited for relation extraction by the use of kernels over structured data for classification. In this paper we present new tree kernels for relation extraction over typed dependency parse trees. On a public benchmark data set we are able to demonstrate a significant improvement in terms of relation extraction quality of our new kernels over other state-of-the-art kernels.