SSF: Sentence Similar Function Based on word3vector Similar Elements


Xinpan Yuan, Songlin Wang, Lanjun Wan, Chengyuan Zhang, Journal of Information Processing Systems Vol. 15, No. 6, pp. 1503-1516, Dec. 2019  

10.3745/JIPS.02.0124
Keywords: Long Sentence Similarity, Similar Element, System Similarity, WMD, Word2vector
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Abstract

In this paper, to improve the accuracy of long sentence similarity calculation, we proposed a sentence similarity calculation method based on a system similarity function. The algorithm uses word2vector as the system elements to calculate the sentence similarity. The higher accuracy of our algorithm is derived from two characteristics: one is the negative effect of penalty item, and the other is that sentence similar function (SSF) based on word2vector similar elements doesn’t satisfy the exchange rule. In later studies, we found the time complexity of our algorithm depends on the process of calculating similar elements, so we build an index of potentially similar elements when training the word vector process. Finally, the experimental results show that our algorithm has higher accuracy than the word mover’s distance (WMD), and has the least query time of three calculation methods of SSF.


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Cite this article
[APA Style]
Xinpan Yuan, Songlin Wang, Lanjun Wan, & Chengyuan Zhang (2019). SSF: Sentence Similar Function Based on word3vector Similar Elements. Journal of Information Processing Systems, 15(6), 1503-1516. DOI: 10.3745/JIPS.02.0124.

[IEEE Style]
X. Yuan, S. Wang, L. Wan and C. Zhang, "SSF: Sentence Similar Function Based on word3vector Similar Elements," Journal of Information Processing Systems, vol. 15, no. 6, pp. 1503-1516, 2019. DOI: 10.3745/JIPS.02.0124.

[ACM Style]
Xinpan Yuan, Songlin Wang, Lanjun Wan, and Chengyuan Zhang. 2019. SSF: Sentence Similar Function Based on word3vector Similar Elements. Journal of Information Processing Systems, 15, 6, (2019), 1503-1516. DOI: 10.3745/JIPS.02.0124.