Literature DB >> 20934991

Graph-based word sense disambiguation of biomedical documents.

Eneko Agirre1, Aitor Soroa, Mark Stevenson.   

Abstract

MOTIVATION: Word Sense Disambiguation (WSD), automatically identifying the meaning of ambiguous words in context, is an important stage of text processing. This article presents a graph-based approach to WSD in the biomedical domain. The method is unsupervised and does not require any labeled training data. It makes use of knowledge from the Unified Medical Language System (UMLS) Metathesaurus which is represented as a graph. A state-of-the-art algorithm, Personalized PageRank, is used to perform WSD.
RESULTS: When evaluated on the NLM-WSD dataset, the algorithm outperforms other methods that rely on the UMLS Metathesaurus alone. AVAILABILITY: The WSD system is open source licensed and available from http://ixa2.si.ehu.es/ukb/. The UMLS, MetaMap program and NLM-WSD corpus are available from the National Library of Medicine https://www.nlm.nih.gov/research/umls/, http://mmtx.nlm.nih.gov and http://wsd.nlm.nih.gov. Software to convert the NLM-WSD corpus into a format that can be used by our WSD system is available from http://www.dcs.shef.ac.uk/∼marks/biomedical_wsd under open source license.

Entities:  

Mesh:

Year:  2010        PMID: 20934991     DOI: 10.1093/bioinformatics/btq555

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  10 in total

1.  Knowledge-based biomedical word sense disambiguation: an evaluation and application to clinical document classification.

Authors:  Vijay N Garla; Cynthia Brandt
Journal:  J Am Med Inform Assoc       Date:  2012-10-16       Impact factor: 4.497

2.  Hyperdimensional computing approach to word sense disambiguation.

Authors:  Bjoern-Toby Berster; J Caleb Goodwin; Trevor Cohen
Journal:  AMIA Annu Symp Proc       Date:  2012-11-03

3.  Evaluating measures of semantic similarity and relatedness to disambiguate terms in biomedical text.

Authors:  Bridget T McInnes; Ted Pedersen
Journal:  J Biomed Inform       Date:  2013-09-04       Impact factor: 6.317

4.  Word sense disambiguation in the clinical domain: a comparison of knowledge-rich and knowledge-poor unsupervised methods.

Authors:  Rachel Chasin; Anna Rumshisky; Ozlem Uzuner; Peter Szolovits
Journal:  J Am Med Inform Assoc       Date:  2014-01-17       Impact factor: 4.497

5.  Clinical concept normalization with a hybrid natural language processing system combining multilevel matching and machine learning ranking.

Authors:  Long Chen; Wenbo Fu; Yu Gu; Zhiyong Sun; Haodan Li; Enyu Li; Li Jiang; Yuan Gao; Yang Huang
Journal:  J Am Med Inform Assoc       Date:  2020-10-01       Impact factor: 4.497

6.  Semantic similarity in the biomedical domain: an evaluation across knowledge sources.

Authors:  Vijay N Garla; Cynthia Brandt
Journal:  BMC Bioinformatics       Date:  2012-10-10       Impact factor: 3.169

7.  Studying the correlation between different word sense disambiguation methods and summarization effectiveness in biomedical texts.

Authors:  Laura Plaza; Antonio J Jimeno-Yepes; Alberto Díaz; Alan R Aronson
Journal:  BMC Bioinformatics       Date:  2011-08-26       Impact factor: 3.169

8.  Exploiting domain information for Word Sense Disambiguation of medical documents.

Authors:  Mark Stevenson; Eneko Agirre; Aitor Soroa
Journal:  J Am Med Inform Assoc       Date:  2011-09-07       Impact factor: 4.497

9.  A learning-based approach for biomedical word sense disambiguation.

Authors:  Hisham Al-Mubaid; Sandeep Gungu
Journal:  ScientificWorldJournal       Date:  2012-05-01

10.  The effect of word sense disambiguation accuracy on literature based discovery.

Authors:  Judita Preiss; Mark Stevenson
Journal:  BMC Med Inform Decis Mak       Date:  2016-07-18       Impact factor: 2.796

  10 in total

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