Graph-based Similarity for Document Retrieval in the Biomedical Domain
Pages 180 - 184
Abstract
The growing amount of available data in the biomedical domain turns out to be beneficial for decision-making, but a sufficiently accurate DR system is required. Plenty of NLP techniques and models have been proposed for semantic similarity in DR, but few of them have been able to consider the variations of the language and relationship between distant words in texts. This work is focused on formulating a Graph-based Similarity for DR method (GBS-DR) for the biomedical domain and comparing the obtained results with traditional DR paradigms. The graph-based methods were selected to prove the importance of analyzing the semantic, syntactic, and long-distant word relationships in texts. It will be demonstrated that through the graph's topology the system can extract the structural information of documents, which solves relevant issues that are faced in this research area.
CCS CONCEPTS • Information Systems • Information Retrieval • Retrieval Models and Ranking • Learning to Rank
References
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- Graph-based Similarity for Document Retrieval in the Biomedical Domain
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Published In
March 2022
291 pages
ISBN:9781450395748
DOI:10.1145/3529399
Copyright © 2022 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 10 June 2022
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ICMLT 2022
ICMLT 2022: 2022 7th International Conference on Machine Learning Technologies
March 11 - 13, 2022
Rome, Italy
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