Relation Extraction: Syntax Generalization and Machine Learning


CatturaGuido Boella – Luigi Di Caro – CDCT Working Paper 19-2013/ELC10

Abstract: In this paper we present a technique to reveal definitions and relations from text. This task has a high impact on all the activities related to the management of legal documents, where any automatic support may represent an essential help. Instead of using pattern matching methods that rely on lexico-syntactic patterns, we propose a technique which only uses syntactic dependencies between terms extracted with a syntactic parser. The assumption is that syntactic information are more robust than patterns when coping with length and complexity of the sentences. Afterwards, we transform such syntactic contexts in abstract representations, that are then fed into a Support Vector Machine classifier. The results on an annotated dataset of definitional sentences demonstrate the validity of our approach overtaking current state-of-the-art techniques.

CDCT WORKING PAPER 19-2013/ELC10
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