Sequential supervised learning for hypernym discovery from Wikipedia
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.
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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