Conference proceeding
Addressing the Variability of Natural Language Expression in Sentence Similarity with Semantic Structure of the Sentences
ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS, v 5476, pp 548-555
01 Jan 2009
Abstract
In this paper, we present a new approach that incorporates semantic structure of sentences, in a form of verb-argument structure, to measure semantic similarity between sentences. The variability of natural language expression makes it difficult for existing text similarity measures to accurately identify semantically similar sentences since sentences conveying the same fact or concept may be composed lexically and syntactically different. Inversely, sentences which are lexically common may not necessarily convey the same meaning. This poses a significant impact on many text mining applications' performance where sentence-level judgment is involved. The evaluation has shown that, by processing sentence at its semantic level, the performance of similarity measures is significantly improved.
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Details
- Title
- Addressing the Variability of Natural Language Expression in Sentence Similarity with Semantic Structure of the Sentences
- Creators
- Palakorn Achananuparp - Drexel UniversityXiaohua Hu - Drexel UniversityChristopher C. Yang - Drexel University
- Contributors
- T Theeramunkong (Editor)B Kijsirikul (Editor)N Cercone (Editor)T B Ho (Editor)
- Publication Details
- ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS, v 5476, pp 548-555
- Series
- Lecture Notes in Artificial Intelligence
- Publisher
- Springer Nature
- Number of pages
- 8
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000268632000049
- Scopus ID
- 2-s2.0-67650688967
- Other Identifier
- 991019170611904721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence