A Study of Using Syntactic Cues in Short-text Similarity Measure

Po-Sen Huang,
Po-Sheng Chiu,
Jia-Wei Chang,
Yueh-Min Huang,
Ming-Che Lee,

Abstract


Short-text semantic similarity is an essential technique of natural language search and is widely used in social network analysis and opinion mining to find unknown knowledge. Such similarity measures usually measure short texts with 10-20 words. Similar to spoken utterances, short texts do not necessarily follow formal grammatical rules. The limited information contained in short texts and their syntactic and semantic flexibility make similarity measures difficult. Therefore, this study designed and tested a part-of-speech-based short-text similarity algorithm to solve those problems. The effects of evaluating different parts of speech are thoroughly discussed. The proposed algorithm achieved the best performance using word measures corresponding to different parts of speech.


Citation Format:
Po-Sen Huang, Po-Sheng Chiu, Jia-Wei Chang, Yueh-Min Huang, Ming-Che Lee, "A Study of Using Syntactic Cues in Short-text Similarity Measure," Journal of Internet Technology, vol. 20, no. 3 , pp. 839-850, May. 2019.

Full Text:

PDF

Refbacks

  • There are currently no refbacks.





Published by Executive Committee, Taiwan Academic Network, Ministry of Education, Taipei, Taiwan, R.O.C
JIT Editorial Office, Library and Information Center, National Dong Hwa University
No. 1, Sec. 2, Da Hsueh Rd. Shoufeng, Hualien 97401, Taiwan, R.O.C.
Tel: +886-3-931-7017  E-mail: jit.editorial@gmail.com