Abstract
Relational similarity measurement between word-pairs is important in many natural language processing tasks such as information extraction and information retrieval. The paper proposes a hybrid approach for relational similarity measurement based on various aspects including term co-occurrence, lexicon-syntactic patterns, as well as their combinations. In this approach, we first extract two relation-term sets from sentences of Wikipedia documents in which two words coincide, and compute the semantic relatedness score of each word-pair in the two relation-term sets. Second, we model the semantic relatedness value of two words together with their frequencies as a point in the three-dimensional space. Afterward, we apply DBSCAN - the classic density-based spatial clustering algorithm to group these 3D points. We finally calculate the similarity based on the clusters. We evaluate this hybrid approach using the well-known 374 SAT analogy questions. The experimental results show that our approach can significantly reduce computational time for measuring relational similarity with a relatively higher score of 52.9% compared to the state-of-the-art.
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Lu, Z., Yan, Z. (2013). A Hybrid Approach for Relational Similarity Measurement. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds) Database Systems for Advanced Applications. DASFAA 2013. Lecture Notes in Computer Science, vol 7826. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37450-0_32
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DOI: https://doi.org/10.1007/978-3-642-37450-0_32
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