doi: 10.4304/jsw.9.9.2401-2408
Web Reviews and Events Matching Based on Event Feature Segments and Semi-Markov Conditional Random Fields
- School of Computer Science and Technology, Shandong University, Jinan, China
Abstract
To establish links between a large number of reviews and events, we propose a web reviews and events matching approach by event feature segments and semi-Markov conditional random fields (CRFs). We extract named entities and verb phrases from reviews as event feature segments. We use semi-Markov CRFs to label the reviews and to recognize event feature segments at the segment level. This approach uses event feature segments to match reviews and events. Therefore, it is more accurate than other approaches which use only named entities to match. We use several feature rules to recognize the variants of named entities, such as abbreviation and acronym. In addition, we use phrase dependency parsing tree to recognize verb phrases. A compositive similarity measurement function is presented to combine similarity results of event feature segments. Experimental results demonstrate that this method can accurately match reviews and events.
Keywords
- event feature segments
- semi-Markov CRFs
How to Cite
Yuanzi Xu, Qingzhong Li, Zhongmin Yan, and Wei Wang, "Web Reviews and Events Matching Based on Event Feature Segments and Semi-Markov Conditional Random Fields," Journal of Software, vol. 9, no. 9, pp. 2401-2408, 2014. https://doi.org/ 10.4304/jsw.9.9.2401-2408
Copyright & License
Copyright © 2014 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).