Name
Abstract | ||
|---|---|---|
To compute the similarity of Chinese sentences accurately, a revised Chinese sentence similarity approach is proposed though enhancing the importance of the modifiers of stem of sentence. After extracting the modified part of the sentence by Language Technology Platform (LTP), this part of each structure could be removed the longest common substring, to better capture the similarities of modified parts. The entire method includes three phases, which are to split the sentences into principal and predicate object structures using the syntactic analysis tool, to generate modifiers and sentence stem vectors and calculate the similarity between the vectors using the Word2Vec, and to obtain the similarity between two sentences by weighting each part. Experimental results on 200 sentences of the LCQMC dataset and corresponding analysis reveal that the proposed method can obtain more accurate similarity calculation results by effectively gaining the modified part - which affects the whole sentence meaning effectively-of the sentence structure. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-06794-5_25 | Artificial Intelligence and Security |
Keywords | DocType | ISSN |
Chinese sentence similarity, Word2Vec, Syntactic structure, Word vector, Natural language processing | Conference | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wang Fangling | 1 | 0 | 0.34 |
| Ye Shaoqiang | 2 | 0 | 0.34 |
| Kang Diwen | 3 | 0 | 0.34 |
| Zain Azlan Mohd | 4 | 0 | 0.68 |
| Zhou Kaiqing | 5 | 0 | 0.34 |