Sequential supervised learning for hypernym discovery from Wikipedia

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Sequential supervised learning for hypernym discovery from Wikipedia is a 2011 conference paper written in English by Litz B., Langer H., Malaka R. and published in Communications in Computer and Information Science.

[edit] Abstract

Hypernym discovery is an essential task for building and extending ontologies automatically. In comparison to the whole Web as a source for information extraction, online encyclopedias provide far more structuredness and reliability. In this paper we propose a novel approach that combines syntactic and lexical-semantic information to identify hypernymic relationships. We compiled semi-automatically and manually created training data and a gold standard for evaluation with the first sentences from the German version of Wikipedia. We trained a sequential supervised learner with a semantically enhanced tagset. The experiments showed that the cleanliness of the data is far more important than the amount of the same. Furthermore, it was shown that bootstrapping is a viable approach to ameliorate the results. Our approach outperformed the competitive lexico-syntactic patterns by 7% leading to an F1-measure of over .91.

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