Difference between revisions of "Relational Similarity Measure: an Approach Combining Wikipedia and Wordnet"
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| + | | title = Relational Similarity Measure: an Approach Combining Wikipedia and Wordnet | ||
| + | | date = 2011 | ||
| + | | authors = [[Yan Jiao Cao]]<br />[[Zhao Lu]]<br />[[Song Mei Cai]] | ||
| + | | doi = 10.4028/www.scientific.net/AMM.55-57.955 | ||
| + | | link = https://www.scientific.net/AMM.55-57.955 | ||
| + | }} | ||
'''Relational Similarity Measure: an Approach Combining Wikipedia and Wordnet''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Yan Jiao Cao]], [[Zhao Lu]] and [[Song Mei Cai]]. | '''Relational Similarity Measure: an Approach Combining Wikipedia and Wordnet''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Yan Jiao Cao]], [[Zhao Lu]] and [[Song Mei Cai]]. | ||
== Overview == | == Overview == | ||
Relational similarities between two pairs of words are the degrees of their semantic relations. Vector Space Model (VSM) is used to measure the relational similarity between two pairs of words, however it needs create patterns manually and these patterns are limited. Recently, Latent Relational Analysis (LRA) is proposed and achieves state-of-art results. However, it is time-consuming and cannot express implicit semantic relations. In this study, authors propose a new approach to measure relational similarities between two pairs of words by combining Wordnet3.0 and the Web-[[Wikipedia]], thus implicit semantic relations from the very large corpus can be mined. The proposed approach mainly possesses two characters: (1) A new method is proposed in the pattern extraction step, which considers various part-of-speeches of words. (2) Wordnet3.0 is applied to calculate the semantic [[relatedness]] between a pair of words so that the implicit semantic relation of the two words can be expressed. Experimental evaluation based on the 374 SAT multiple-choice word-analogy questions, the precision of the proposed approach is 43.9%, which is lower than that of LRA suggested by Turney in 2005, but the suggested approach mainly focuses on mining the semantic relations among words. | Relational similarities between two pairs of words are the degrees of their semantic relations. Vector Space Model (VSM) is used to measure the relational similarity between two pairs of words, however it needs create patterns manually and these patterns are limited. Recently, Latent Relational Analysis (LRA) is proposed and achieves state-of-art results. However, it is time-consuming and cannot express implicit semantic relations. In this study, authors propose a new approach to measure relational similarities between two pairs of words by combining Wordnet3.0 and the Web-[[Wikipedia]], thus implicit semantic relations from the very large corpus can be mined. The proposed approach mainly possesses two characters: (1) A new method is proposed in the pattern extraction step, which considers various part-of-speeches of words. (2) Wordnet3.0 is applied to calculate the semantic [[relatedness]] between a pair of words so that the implicit semantic relation of the two words can be expressed. Experimental evaluation based on the 374 SAT multiple-choice word-analogy questions, the precision of the proposed approach is 43.9%, which is lower than that of LRA suggested by Turney in 2005, but the suggested approach mainly focuses on mining the semantic relations among words. | ||
Revision as of 07:33, 6 July 2019
Authors | Yan Jiao Cao Zhao Lu Song Mei Cai |
|---|---|
Publication date | 2011 |
DOI | 10.4028/www.scientific.net/AMM.55-57.955 |
Links | Original |
Relational Similarity Measure: an Approach Combining Wikipedia and Wordnet - scientific work related to Wikipedia quality published in 2011, written by Yan Jiao Cao, Zhao Lu and Song Mei Cai.
Overview
Relational similarities between two pairs of words are the degrees of their semantic relations. Vector Space Model (VSM) is used to measure the relational similarity between two pairs of words, however it needs create patterns manually and these patterns are limited. Recently, Latent Relational Analysis (LRA) is proposed and achieves state-of-art results. However, it is time-consuming and cannot express implicit semantic relations. In this study, authors propose a new approach to measure relational similarities between two pairs of words by combining Wordnet3.0 and the Web-Wikipedia, thus implicit semantic relations from the very large corpus can be mined. The proposed approach mainly possesses two characters: (1) A new method is proposed in the pattern extraction step, which considers various part-of-speeches of words. (2) Wordnet3.0 is applied to calculate the semantic relatedness between a pair of words so that the implicit semantic relation of the two words can be expressed. Experimental evaluation based on the 374 SAT multiple-choice word-analogy questions, the precision of the proposed approach is 43.9%, which is lower than that of LRA suggested by Turney in 2005, but the suggested approach mainly focuses on mining the semantic relations among words.
