A Fuzzy Word Similarity Measure for Selecting Top $k$ Similar Words in Query Expansion
Top <inline formula><tex math notation="LaTeX" k$/tex math>/inline formula> words selection is a technique used to detect and return the <inline formula><tex math notation="LaTeX" k$/tex math>/inline formula> 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 <inline formula><tex math notation="LaTeX"$k$/tex math>/inline formula> 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 <italic>k</italic> 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.
IEEE Transactions on Fuzzy Systems