A Fuzzy Word Similarity Measure for Selecting Top-k Similar Words in Query Expansion
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Fuzzy Systems, 2021, 29, (8), pp. 2132-2144
- Issue Date:
- 2021-01-01
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Filename | Description | Size | |||
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A_Fuzzy_Word_Similarity_Measure_for_Selecting_Top-k_Similar_Words_in_Query_Expansion.pdf | Published version | 1.24 MB |
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Top-k words selection is a technique used to detect and return the k most similar words to a given word from a candidate set. This is a crucial and widely used tool in various tasks. The key issue in top-k words selection is how to measure the similarity between words. One popular and effective solution is to use a word embedding-based similarity measure, which represents words as low-dimensional vectors and measures the similarities between words according to the similarity of the vectors, using a metric. However, most word embedding methods only consider the local proximity properties of two words in a corpus. To mitigate this issue. In this article, we propose to use association rules for measuring word similarity at a global level, and a fuzzy similarity measure for top-k words selection that jointly encodes the local and the global similarities. Experiments on a real-world query task with three benchmark datasets, i.e., TREC-disk 4&5, WT10G, and RCV1, demonstrate the efficiency of the proposed method compared to several state-of-the-art baselines.
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