Conference proceeding
The evaluation of sentence similarity measures
DATA WAREHOUSING AND KNOWLEDGE DISCOVERY, PROCEEDINGS, v 5182, pp 305-316
01 Jan 2008
Abstract
The ability to accurately judge the similarity between natural language sentences is critical to the performance of several applications such as text mining, question answering, and text summarization. Given two sentences, an effective similarity measure should be able to determine whether the sentences are semantically equivalent or not, taking into account the variability of natural language expression. That is, the correct similarity judgment should be made even if the sentences do not share similar surface form. In this work, we evaluate fourteen existing text similarity measures which have been used to calculate similarity score between sentences in many text applications. The evaluation is conducted on three different data sets, TREC9 question variants, Microsoft Research paraphrase corpus, and the third recognizing textual entailment data set.
Metrics
Details
- Title
- The evaluation of sentence similarity measures
- Creators
- Palakorn Achananuparp - Drexel UniversityXiaohua Hu - Drexel UniversityXiajiong Shen - College of Computer and Information Engineering, Hehan University, Henan, China
- Contributors
- I Y Song (Editor)J Eder (Editor)T M Nguyen (Editor)
- Publication Details
- DATA WAREHOUSING AND KNOWLEDGE DISCOVERY, PROCEEDINGS, v 5182, pp 305-316
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Nature
- Number of pages
- 3
- Grant note
- 240205; 240196 / PA Dept of Health Tobacco Settlement Formula IIS 0448023; CCF 0514679 / NSF Career; National Science Foundation (NSF); NSF - Office of the Director (OD) 239667 / PA Dept of Health
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000259488400029
- Scopus ID
- 2-s2.0-52949135206
- Other Identifier
- 991019170380104721
InCites Highlights
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- Collaboration types
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Computer Science, Theory & Methods