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Combined Syntactic and Semantic Kernels for Text Classification


Stephan Bloehdorn, Alessandro Moschitti



Published: 2007 April
Herausgeber: Gianni Amati, Claudio Carpineto, Gianni Romano
Buchtitel: Advances in Information Retrieval - Proceedings of the 29th European Conference on Information Retrieval (ECIR 2007), 2-5 April 2007, Rome, Italy
Ausgabe: 4425
Nummer: 4425
Reihe: Lecture Notes in Computer Science
Seiten: 307-318
Verlag: Springer
Referierte Veröffentlichung
BibTeX

Kurzfassung
The exploitation of syntactic structures and semantic background knowledge has always been an appealing subject in the context of text retrieval and information management. The usefulness of this kind of information has been shown most prominently in highly specialized tasks, such as classification in Question Answering (QA) scenarios. So far, however, additional syntactic or semantic information has been used only individually. In this paper, we propose a principled approach for jointly exploiting both types of information. We propose a new type of kernel, the Semantic Syntactic Tree Kernel (SSTK), which incorporates linguistic structures, e.g. syntactic dependencies, and semantic background knowledge, e.g. term similarity based on WordNet, to automatically learn question categories in QA. We show the power of this approach in a series of experiments with a well known Question Classification dataset.

ISBN: 978-3-540-71494-1
Weitere Informationen unter: Link

Projekt

X-MediaSEKT



Forschungsgruppe

Wissensmanagement


Forschungsgebiet
Text Mining, Maschinelles Lernen


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