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A. Akbik

Paper on Unsupervised Relation Extraction accepted at COLING Conference 2012

A paper titled "Unsupervised Discovery of Relations and Discriminative Extraction Patterns" by Alan Akbik, Larysa Visengeriyeva, Priska Herger, Holmer Hemsen and Alexander Löser has been accepted at the 24th International Conference on Computational Linguistics 2012 in Mumbai, India.


Unsupervised Relation Extraction (URE) is the task of extracting relations of a priori unknown semantic types using clustering methods on a vector space model of entity pairs and patterns. In this paper, we show that an informed feature generation technique based on dependency trees significantly improves clustering quality, as measured by the F-score, and therefore the ability of the URE method to discover relations in text. Furthermore, we extend URE to produce a set of weighted patterns for each identified relation that can be used by an information extraction system to find further instances of this relation. Each pattern is assigned to one or multiple relations with different confidence strengths, indicating how reliably a pattern evokes a relation, using the theory of Discriminative Category Matching. We evaluate our findings in two tasks against strong baselines and show significant improvements both in relation discovery and information extraction.