Taxonomy learning is a prerequisite step for ontology learning. In order to create a taxonomy, first of all, existing 'is-a' relations between words should be extracted. A known way to extract 'is-a' relations is finding lexicosyntactic patterns in large text corpus. Although this approach produces results with high precision but it suffers from low values of recall. Furthermore developing a comprehensive set of patterns is tedious and cumbersome. In this paper, firstly, we introduce an approach for developing lexico-syntactic patterns automatically using the snippets of search engine results and then, challenge the law recall of this approach using a combined model, which is based on cooccurrence of pair words in the web and neural network classifier. Using our approach both precision and recall of extracted 'is-a' relations improved and FMeasure value reaches 0.72. © 2008 Springer-Verlag.
CITATION STYLE
Neshati, M., Abolhassani, H., & Fatemi, H. (2008). Automatic extraction of is-a relations in taxonomy learning. In Communications in Computer and Information Science (Vol. 6 CCIS, pp. 17–24). https://doi.org/10.1007/978-3-540-89985-3_3
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