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W. Yih, C. Meek
Improving Similarity Measures for Short Segments of Text
AAAI-07
In this paper we improve previous work on measuring the similarity of short segments of text in two ways. First, we introduce a Web-relevance similarity measure and demonstrate its effectiveness. This measure extends the Web-kernel similarity function introduced by Sahami and Heilman (2006) by using relevance weighted inner-product of term occurrences rather than TF×IDF. Second, we show that one can further improve the accuracy of similarity measures by using a machine learning approach. Our methods outperform other state-of-the-art methods in a general query suggestion task for multiple evaluation metrics. @InProceedings{YihMe07,
author = {W. Yih and C. Meek},
title = {Improving Similarity Measures for Short Segments of Text},
booktitle = {Proceedings of AAAI 2007},
pages = {1489-1494},
year = {2007}
} |
