Abstract
Hypernym/hyponym relation extraction plays an essential role in taxonomy learning. The conventional methods based on lexico-syntactic patterns or machine learning usually make use of content-related features. In this paper, we find that the proportions of hyperlinks with different semantic type vary markedly in different network motifs. Based on this observation, we propose MOTIF-RE, an algorithm of extracting hypernym/hyponym relation from Wikipedia hyperlinks. The extraction process consists of three steps: 1) Build a directed graph from a set of domain-specific Wikipedia articles. 2) Count the occurrences of hyperlinks in every three-node network motif and create a feature vector for every hyperlink. 3) Train a classifier to identify semantic relation of hyperlinks. We created three domain-specific Wikipedia article sets to test MOTIF-RE. Experiments on individual dataset show that MOTIF-RE outperforms the baseline algorithm by about 30% in terms of F1-measure. Cross-domain experimental results show similar, which proves that MOTIF-RE has fairly good domain adaptation ability.
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Wei, B., Liu, J., Ma, J., Zheng, Q., Zhang, W., Feng, B. (2012). MOTIF-RE: Motif-Based Hypernym/Hyponym Relation Extraction from Wikipedia Links. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34500-5_72
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DOI: https://doi.org/10.1007/978-3-642-34500-5_72
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