Acquiring IE Patterns through Distributional Lexical Semantic Models

Techniques for the automatic acquisition of Information Extraction Pattern are still a crucial issue in knowledge engineering. A semi supervised learning method, based on large scale linguistic resources, such as FrameNet and WordNet, is discussed. In particular, a robust method for assigning conceptual relations (i.e. roles) to relevant grammatical structures is defined according to distributional models of lexical semantics over a large scale corpus. Experimental results show that the use of the resulting knowledge base provide significant results, i.e. correct interpretations for about 90% of the covered sentences. This confirms the impact of the proposed approach on the quality and development time of large scale IE systems.