Abstract
Relation representation plays an important role in text understanding. In this paper, different from previously published supervised methods or semi-supervised methods, an new method of relation representation and clustering based on shortest path and word vector was proposed. By accumulating the word vector along the shortest path within dependency tree, we can not only obtain the essential representation of the relation, but also can map the relation into semantic space simultaneously. Therefore, reliable distance between any two relations could be measured. Moreover, further applications such as relations clustering can be performed conveniently by direct analysis on the collection of vectors.
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Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, Lake Tahoe, Nevada, United States, pp. 3111–3119 (2013)
Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd. edn. MIT Press (2009)
Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. IJCAI 7, 1606–1611 (2007)
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Wang, X., Xiao, Y., Wang, W. (2015). Shortest Path and Word Vector Based Relation Representation and Clustering. In: Dong, X., Yu, X., Li, J., Sun, Y. (eds) Web-Age Information Management. WAIM 2015. Lecture Notes in Computer Science(), vol 9098. Springer, Cham. https://doi.org/10.1007/978-3-319-21042-1_66
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DOI: https://doi.org/10.1007/978-3-319-21042-1_66
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