Journal Article10.1109/TKDE.2003.1209005
An approach for measuring semantic similarity between words using multiple information sources
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TL;DR: This paper explores the determination of semantic similarity by a number of information sources, which consist of structural semantic information from a lexical taxonomy and information content from a corpus.
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Abstract: Semantic similarity between words is becoming a generic problem for many applications of computational linguistics and artificial intelligence. This paper explores the determination of semantic similarity by a number of information sources, which consist of structural semantic information from a lexical taxonomy and information content from a corpus. To investigate how information sources could be used effectively, a variety of strategies for using various possible information sources are implemented. A new measure is then proposed which combines information sources nonlinearly. Experimental evaluation against a benchmark set of human similarity ratings demonstrates that the proposed measure significantly outperforms traditional similarity measures.
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Citations
Sentence similarity based on semantic nets and corpus statistics
TL;DR: Experiments demonstrate that the proposed method provides a similarity measure that shows a significant correlation to human intuition and can be used in a variety of applications that involve text knowledge representation and discovery.
Measuring Semantic Similarity between Words Using Web Search Engines
Danushka Bollegala,Yutaka Matsuo,Mitsuru Ishizuka +2 more
- 01 Jan 2007
TL;DR: A robust semantic similarity measure that uses the information available on the Web to measure similarity between words or entities and a novel approach to compute semantic similarity using automatically extracted lexico-syntactic patterns from text snippets is proposed.
Semantic text similarity using corpus-based word similarity and string similarity
Aminul Islam,Diana Inkpen +1 more
TL;DR: A method for measuring the semantic similarity of texts using a corpus-based measure of semantic word similarity and a normalized and modified version of the Longest Common Subsequence string matching algorithm is presented.
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Ontology-based semantic similarity: A new feature-based approach
TL;DR: This paper survey and classify most of the ontology-based approaches developed in order to evaluate their advantages and limitations and compare their expected performance both from theoretical and practical points of view, and presents a new ontological-based measure relying on the exploitation of taxonomical features.
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•Proceedings Article
UMBC_EBIQUITY-CORE: Semantic Textual Similarity Systems
Lushan Han,Abhay L. Kashyap,Tim Finin,James Mayfield,Jonathan Weese +4 more
- 13 Jun 2013
TL;DR: Three semantic text similarity systems developed for the *SEM 2013 STS shared task used a simple term alignment algorithm augmented with penalty terms, and two used support vector regression models to combine larger sets of features.
References
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Michael Sussna
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TL;DR: This work investigates using the massive Word Net semantic network for disambiguation during document indexing to improve precision and improvement in disamblguation compared with chance.