| Hermann Helbig|
(Alternative names for this author)
|Co-authors||Bj PelzerÃ¶rn, GlÃ¶Ingo ckner, Ulrich Furbach, Vor Der Bruck T.|
|Authorship||Publications (2), datasets (0), tools (0)|
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|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Logic-Based Question Answering||KI - KÃ¼nstliche Intelligenz||2010||0||0|
|Validating meronymy hypotheses with support vector machines and graph kernels||Graph kernel
Support vector machine
|Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010||English||2010||There is a substantial body of work on the extraction of relations from texts, most of which is based on pattern matching or on applying tree kernel functions to syntactic structures. Whereas pattern application is usually more efficient, tree kernels can be superior when assessed by the F-measure. In this paper, we introduce a hybrid approach to extracting meronymy relations, which is based on both patterns and kernel functions. In a first step, meronymy relation hypotheses are extracted from a text corpus by applying patterns. In a second step these relation hypotheses are validated by using several shallow features and a graph kernel approach. In contrast to other meronymy extraction and validation methods which are based on surface or syntactic representations we use a purely semantic approach based on semantic networks. This involves analyzing each sentence of the Wikipedia corpus by a deep syntactico-semantic parser and converting it into a semantic network. Meronymy relation hypotheses are extracted from the semantic networks by means of an automated theorem prover, which employs a set of logical axioms and patterns in the form of semantic networks. The meronymy candidates are then validated by means of a graph kernel approach based on common walks. The evaluation shows that this method achieves considerably higher accuracy, recall, and F-measure than a method using purely shallow validation.||0||0|