An IR Similarity Measure which is Tolerant for Morphological Variation.
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概要
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In this paper, we propose a measure for information retrieval (IR). This measure is tolerant for morphological variation. When various persons describe the data to retrieve, their notations may vary even if the data describe the same topic. This variation prevents system to retrieve all of relevant documents for the input sentence. Although human can handle this variation, computers usually can not handle this. Edit distance is a well-known measure that can cope with this variation. We have used this measure for information retrieval and found that its precision is poor. Therefore, we propose to modify this similarity measure to be suitable for information retrieval. We show that this extension improves the performance. We also compared the proposed similarity measure with the popular similarity measures used in many information retrieval systems.
- 言語処理学会の論文
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