Abstract
As for ontology construction from Chinese text, the non-hierarchical relation extraction is harder than the concept extraction and its extraction effect is still not satisfactory. In this paper, we put forward a scalable corpus model, which uses Tongyici Cilin and word2vec to calculate terms’ similarity and add the qualified candidate terms to the corpora. In this way we can expand the scalable corpus while extracting non-hierarchical relations. In turn, the scalable corpus that has been expanded with the new terms will facilitate the non-hierarchical relation extraction further. We carry out the experiment with Chinese texts in the domain of Computer, whose results show that with expansion of the corpus, the extraction effect will be better and better.
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Notes
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In the paper, the concept refers to some concept or the instance of some concept.
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Acknowledgments
Hai Wan’s research was in part supported by the National Natural Science Foundation of China under grant 61573386, Natural Science Foundation of Guangdong Province under grant 2016A030313292, Guangdong Province Science and Technology Plan projects under grant 2016B030305007, and Sun Yat-sen University Young Teachers Cultivation Project under grant 16lgpy40.
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Su, X., Wan, H., Chen, R., Liu, Q., Zhang, W., Du, J. (2016). Non-hierarchical Relation Extraction of Chinese Text Based on Scalable Corpus. In: Li, YF., et al. Semantic Technology. JIST 2016. Lecture Notes in Computer Science(), vol 10055. Springer, Cham. https://doi.org/10.1007/978-3-319-50112-3_17
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DOI: https://doi.org/10.1007/978-3-319-50112-3_17
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