Abstract
Concepts are used to solve the term-mismatch problem. However, we need an effective similarity measure between concepts. Word embedding presents a promising solution. We present in this study three approaches to build concepts vectors based on words vectors. We use a vector-based measure to estimate inter-concepts similarity. Our experiments show promising results. Furthermore, words and concepts become comparable. This could be used to improve conceptual indexing process.
Cite
CITATION STYLE
Abdulahhad, K. (2018). Concept embedding for information retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10772 LNCS, pp. 563–569). Springer Verlag. https://doi.org/10.1007/978-3-319-76941-7_45
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