Abstract
This paper presents a contextual semantic-based approach for expansion of an initial lexicon containing domain-centric seed words. Starting with a small lexicon containing some domain-centric seed words, the proposed approach models text corpus as a weighted word-graph, where the initial weight of a node (word) represents the contextual semantic-based association between the node and the target domain, and the weight of an edge represents the co-occurrence frequency of the respective nodes. The semantic-based association between a node and the target domain is calculated as a function of three contextual semantic-based association metrics. Thereafter, a random walk-based modified PageRank algorithm is applied on the weighted graph to rank and select the most relevant terms for domain-centric lexicon expansion. The proposed approach is evaluated over five datasets, and found to perform significantly better than three baselines and three state-of-the-art approaches.
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Acknowledgment
The authors would like to thank the South Asian University, Delhi, for the financial support under the start-up research grant provided to the first author.
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Abulaish, M., Fazil, M., Anwar, T. (2020). A Contextual Semantic-Based Approach for Domain-Centric Lexicon Expansion. In: Borovica-Gajic, R., Qi, J., Wang, W. (eds) Databases Theory and Applications. ADC 2020. Lecture Notes in Computer Science(), vol 12008. Springer, Cham. https://doi.org/10.1007/978-3-030-39469-1_18
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DOI: https://doi.org/10.1007/978-3-030-39469-1_18
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