Back to Search
Start Over
A Fuzzy Word Similarity Measure for Selecting Top-k Similar Words in Query Expansion.
- Source :
- IEEE Transactions on Fuzzy Systems; Aug2021, Vol. 29 Issue 8, p2132-2144, 13p
- Publication Year :
- 2021
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- FUZZY measure theory
FUZZY sets
NATURAL language processing
Subjects
Details
- Language :
- English
- ISSN :
- 10636706
- Volume :
- 29
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Fuzzy Systems
- Publication Type :
- Academic Journal
- Accession number :
- 153127520
- Full Text :
- https://doi.org/10.1109/TFUZZ.2020.2993702