NDLI: Semantic similarity measure in biomedical domain leverage Web Search Engine

Content Provider	IEEE Xplore Digital Library
Author	Chi-Huang Chen Sheau-Ling Hsieh Yung-Ching Weng Wen-Yung Chang Feipei Lai
Copyright Year	2010
Description	Author affiliation: Department of Electrical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617 Taiwan (Chi-Huang Chen) \|\| Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University, Taiwan (Feipei Lai) \|\| EECS International Graduate Program Institute/IT Center, National Chiao Tung University, No. 1001 University Road, Hsinchu, Taiwan (Sheau-Ling Hsieh) \|\| Department of Computer Science and Engineering, National Taiwan University, Taiwan (Yung-Ching Weng) \|\| Institute of Computer Science and Engineering, College of Computer Science, National Chiao Tung University, Taiwan (Wen-Yung Chang)
Abstract	Semantic similarity measure plays an essential role in Information Retrieval and Natural Language Processing. In this paper we propose a page-count-based semantic similarity measure and apply it in biomedical domains. Previous researches in semantic web related applications have deployed various semantic similarity measures. Despite the usefulness of the measurements in those applications, measuring semantic similarity between two terms remains a challenge task. The proposed method exploits page counts returned by the Web Search Engine. We define various similarity scores for two given terms P and Q, using the page counts for querying P, Q and P AND Q. Moreover, we propose a novel approach to compute semantic similarity using lexico-syntactic patterns with page counts. These different similarity scores are integrated adapting support vector machines, to leverage the robustness of semantic similarity measures. Experimental results on two datasets achieve correlation coefficients of 0.798 on the dataset provided by A. Hliaoutakis, 0.705 on the dataset provide by T. Pedersen with physician scores and 0.496 on the dataset provided by T. Pedersen et al. with expert scores.
Starting Page	4436
Ending Page	4439
File Size	436265
Page Count	4
File Format	PDF
ISBN	9781424441235
ISSN	1557170X
DOI	10.1109/IEMBS.2010.5626008
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Argentina
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Semantics Biomedical measurements Correlation Kernel Training Medical services Support vector machine classification
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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