1997 SemanticSimBasedOnCorpusStats
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- (Jiang & Conrath, 1997) ⇒ Jay J. Jiang, and David W. Conrath. (1997). “Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy.” In: Proceedings on International Conference on Research in Computational Linguistics (ROCLING X).
Subject Headings: Jiang-Conrath Similarity Measure, Word Sense Disambiguation Algorithm, Lexical Semantic Similarity Function.
Notes
- It proposes a measure of semantic similarity between word pairs that combines statistical and lexical information (Turney, 2001).
Cited By
2001
- (Turney, 2001) ⇒ Peter D. Turney. (2001). “Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL.” In: Proceedings of the 12th European Conference on Machine Learning (ECML 2001). doi:10.1007/3-540-44795-4_42
- QUOTE: Various measures of semantic similarity between word pairs have been proposed, some using statistical (unsupervised learning from text) techniques [16, 17, 18], some using lexical databases (hand-built) [19, 20], and some hybrid approaches, combining statistics and lexical information [21, (Jiang & Conrath, 1997)]. Statistical techniques typically suffer from the sparse data problem: they perform poorly when the words are relatively rare, due to the scarcity of data. Hybrid approaches attempt to address this problem by supplementing sparse data with information from a lexical database [21, (Jiang & Conrath, 1997)].
Quotes
Abstract
This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human subjects replicate the same task.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1997 SemanticSimBasedOnCorpusStats | Jay J. Jiang David W. Conrath | Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy | Proceedings on International Conference on Research in Computational Linguistics | 1997 |