| Issue |
MATEC Web Conf.
Volume 309, 2020
2019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
|
|
|---|---|---|
| Article Number | 03020 | |
| Number of page(s) | 8 | |
| Section | Smart Algorithms and Recognition | |
| DOI | https://doi.org/10.1051/matecconf/202030903020 | |
| Published online | 04 March 2020 | |
A conceptual similarity and correlation discrimination method based on HowNet
School of Computer Science and Technology, Liaocheng University, Liaocheng, China
* Corresponding author: dingyunnian3298@163.com
The similarity and correlation analysis of word concepts has a wide range of applications in natural language processing, and has important research significance in information retrieval, text classification, data mining, and other application fields. This paper analyzes and summarizes the information of sememes relationship through the definition of words in HowNet and proposes a method to distinguish the similarity and correlation of words. Firstly, using a combination of the part of speech and sememes to distinguish the similarity and correlation between words concept. Secondly, the similarity and correlation calculation results between vocabulary concepts are used to further optimize the judgment results. Finally, the similarity and correlation distinction and discrimination between vocabulary concepts are realized. The experimental results show that the method reduces the complexity of the algorithm and greatly improves the work efficiency. The semantic similarity and correlation judgment results are more in line with the human intuitive experience and improve the accuracy of computer understanding of natural language. which provides an important theoretical basis for the development of natural language.
Key words: Similarity / Relevance / Natural language processing / HowNet
© The Authors, published by EDP Sciences, 2020
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.
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.