ArticleJournal of integrative bioinformatics2017
Supervised Learning and Knowledge-Based Approaches Applied to Biomedical Word Sense Disambiguation.
Article in Journal of integrative bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Temporal disambiguation of relative temporal expressions in clinical texts.Frontiers in research metrics and analytics · 2022Article
- Improving broad-coverage medical entity linking with semantic type prediction and large-scale datasets.Journal of biomedical informatics · 2021Article
- An Efficient Parallelized Ontology Network-Based Semantic Similarity Measure for Big Biomedical Document Clustering.Computational and mathematical methods in medicine · 2021Article
- deepBioWSD: effective deep neural word sense disambiguation of biomedical text data.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Extraction of chemical-protein interactions from the literature using neural networks and narrow instance representation.Database : the journal of biological databases and curation · 2019Article
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Authors and funding
2 authors.
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Abstract
Word sense disambiguation (WSD) is an important step in biomedical text mining, which is responsible for assigning an unequivocal concept to an ambiguous term, improving the accuracy of biomedical information extraction systems. In this work we followed supervised and knowledge-based disambiguation approaches, with the best results obtained by supervised means. In the supervised method we used bag-of-words as local features, and word embeddings as global features. In the knowledge-based method we combined word embeddings, concept textual definitions extracted from the UMLS database, and concept association values calculated from the MeSH co-occurrence counts from MEDLINE articles. Also, in the knowledge-based method, we tested different word embedding averaging functions to calculate the surrounding context vectors, with the goal to give more importance to closest words of the ambiguous term. The MSH WSD dataset, the most common dataset used for evaluating biomedical concept disambiguation, was used to evaluate our methods. We obtained a top accuracy of 95.6 % by supervised means, while the best knowledge-based accuracy was 87.4 %. Our results show that word embedding models improved the disambiguation accuracy, proving to be a powerful resource in the WSD task.
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