標題: Semantic Similarity Measure in Biomedical Domain Leverage Web Search Engine
作者: Chen, Chi-Huang
Hsieh, Sheau-Ling
Weng, Yung-Ching
Chang, Wen-Yung
Lai, Feipei
資訊工程學系
Department of Computer Science
公開日期: 2010
摘要: 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.
URI: http://dx.doi.org/10.1109/IEMBS.2010.5626008
http://hdl.handle.net/11536/135560
ISBN: 978-1-4244-4124-2
ISSN: 1557-170X
DOI: 10.1109/IEMBS.2010.5626008
期刊: 2010 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
起始頁: 4436
結束頁: 4439
Appears in Collections:Conferences Paper