| Issue |
MATEC Web Conf.
Volume 139, 2017
2017 3rd International Conference on Mechanical, Electronic and Information Technology Engineering (ICMITE 2017)
|
|
|---|---|---|
| Article Number | 00028 | |
| Number of page(s) | 5 | |
| DOI | https://doi.org/10.1051/matecconf/201713900028 | |
| Published online | 05 December 2017 | |
Sentence Similarity Research Based on Chinese FrameNet and Semantic Dependency Parsing
1 College of information technology and cyber security, People’s Public Security University of China, Beijing, China
According to the problems existing in the similarity comparison of Chinese sentences,this paper proposed a sentence similarity computing method which combined with advantages of Chinese frameNet method and semantic dependency parsing method.This method is based on the framework of semantic.And firstly,the method analyzed and calculated the similarity of two frameworks;Further,it analyzed semantic dependency relationship existing in the core frame elements from the two aspects of the meaning and the overall dependency relation;Finally,it put forward the fusion sentence similarity calculation formula.Experimental results show that compared with the method based on space vector model and based on HowNet and based on Frame Semantic Parsing, this method has higher accuracy in the similarity judgment of Chinese sentences.
Key words: Chinese frameNet; / semantic dependency parsing ; / sentence similarity
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.