Graph-based Representation for Sentence Similarity Measure : A Comparative Analysis
Textual data are a rich source of knowledge hence, sentence comparison has become one of the important tasks in text mining related works.Most previous work in text comparison are performed at document level, research suggest that comparing sentence level text is a non-trivial problem.One of the rea...
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| 主要な著者: | , , , , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Science Publishing Corporation Inc
2018
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| 主題: | |
| オンライン･アクセス: | http://repo.uum.edu.my/24418/1/IJET%207%202.14%202018%2032%2035.pdf http://repo.uum.edu.my/24418/ http://doi.org/10.14419/ijet.v7i2.14.11149 |
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| 要約: | Textual data are a rich source of knowledge hence, sentence comparison has become one of the important tasks in text mining related works.Most previous work in text comparison are performed at document level, research suggest that comparing sentence level text is a non-trivial problem.One of the reason is two sentences can convey the same meaning with totally dissimilar words.This paper presents the results of a comparative analysis on three representation schemes i.e. term frequency inverse document frequency, Latent Semantic Analysis and Graph based representation using three similarity measures i.e. Cosine, Dice coefficient and Jaccard similarity to compare the similarity of sentences.Results reveal that the graph based representation and the Jaccard similarity measure outperforms the others in terms of precision, recall and F-measures. |
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