Paper Info
Title
Supervised Learning and Knowledge-Based Approaches Applied to Biomedical Word Sense Disambiguation.
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.
Year
DOI
Venue
2017
10.1515/jib-2017-0051
JOURNAL OF INTEGRATIVE BIOINFORMATICS
Keywords
Field
DocType
Biomedical text mining,information extraction,word embeddings
Data mining,Computer science,Databases as Topic,Supervised learning,Biomedical text mining,Information extraction,Natural language processing,Artificial intelligence,Semantics,Word-sense disambiguation
Journal
Volume
Issue
ISSN
14
SP4
1613-4516
Citations 
PageRank 
References 
1
0.35
2
Authors
2
Name
Order
Citations
PageRank
Antunes, R.122.40
Sérgio Matos241529.51