JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
A Text Similarity Measurement Method Based on Singular Value Decomposition and Semantic Relevance |
| 영문제목(English Title) |
A Text Similarity Measurement Method Based on Singular Value Decomposition and Semantic Relevance |
| 저자(Author) |
Xu Li
Chunlong Yao
Fenglong Fan
Xiaoqiang Yu
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| 원문수록처(Citation) |
VOL 13 NO. 04 PP. 0863 ~ 0875 (2017. 08) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
The traditional text similarity measurement methods based on word frequency vector ignore the semantic relationships between words, which has become the obstacle to text similarity calculation, together with the high-dimensionality and sparsity of document vector. To address the problems, the improved singular value decomposition is used to reduce dimensionality and remove noises of the text representation model. The optimal number of singular values is analyzed and the semantic relevance between words can be calculated in constructed semantic space. An inverted index construction algorithm and the similarity definitions between vectors are proposed to calculate the similarity between two documents on the semantic level. The experimental results on benchmark corpus demonstrate that the proposed method promotes the evaluation metrics of F-measure.
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| 키워드(Keyword) |
Natural Language Processing
Semantic Relevance
Singular Value Decomposition
Text Representation
Text Similarity Measurement
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| 파일첨부 |
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