Disambiguation of biomedical abbreviations

Stevenson, Mark; Guo, Yikun; Alamri, Abdulaziz; Gaizauskas, Robert

doi:10.3115/1572364.1572374

Cited by 41 publications

(25 citation statements)

References 21 publications

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“…The authors did not find this to be the case when evaluating it on the NLM-WSD dataset [33]. In the future, we would like to evaluate this method on additional datasets such as NLM-WSD and the Abbrev dataset [34]. …”

Section: Discussionmentioning

confidence: 99%

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

McInnes

Pedersen

2013

Journal of Biomedical Informatics

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Introduction In this article, we evaluate a knowledge-based word sense disambiguation method that determines the intended concept associated with an ambiguous word in biomedical text using semantic similarity and relatedness measures. These measures quantify the degree of similarity or relatedness between concepts in the Unified Medical Language System (UMLS). The objective of this work is to develop a method that can disambiguate terms in biomedical text by exploiting similarity and relatedness information extracted from biomedical resources and to evaluate the efficacy of these measure on WSD. Method We evaluate our method on a biomedical dataset (MSH-WSD) that contains 203 ambiguous terms and acronyms. Results We show that information content-based measures derived from either a corpus or taxonomy obtain a higher disambiguation accuracy than path-based measures or relatedness measures on the MSH-WSD dataset. Availability The WSD system is open source and freely available from http://search.cpan.org/dist/UMLS-SenseRelate/. The MSH-WSD dataset is available from the National Library of Medicine http://wsd.nlm.nih.gov.

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Section: Discussionmentioning

confidence: 99%

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

McInnes

Pedersen

2013

Journal of Biomedical Informatics

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“…This technique filters potentially irrelevant documents that mention the gene names in some other context, by creating language models for all the senses and assigning the closest sense to an ambiguous name. Similar methods have been described for disambiguating biomedical abbreviations by taking into consideration the context in which the abbreviations occur (10–13). …”

Section: Methodsmentioning

confidence: 99%

eFIP: A Tool for Mining Functional Impact of Phosphorylation from Literature

Arighi

Siu

Tudor

et al. 2010

Methods in Molecular Biology

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Technologies and experimental strategies have improved dramatically in the field of genomics and proteomics facilitating analysis of cellular and biochemical processes, as well as of proteins networks. Based on numerous such analyses, there has been a significant increase of publications in life sciences and biomedicine. In this respect, knowledge bases are struggling to cope with the literature volume and they may not be able to capture in detail certain aspects of proteins and genes. One important aspect of proteins is their phosphorylated states and their implication in protein function and protein interacting networks. For this reason, we developed eFIP, a web-based tool, which aids scientists to find quickly abstracts mentioning phosphorylation of a given protein (including site and kinase), coupled with mentions of interactions and functional aspects of the protein. eFIP combines information provided by applications such as eGRAB, RLIMS-P, eGIFT and AIIAGMT, to rank abstracts mentioning phosphorylation, and to display the results in a highlighted and tabular format for a quick inspection. In this chapter, we present a case study of results returned by eFIP for the protein BAD, which is a key regulator of apoptosis that is posttranslationally modified by phosphorylation.

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“…The task of abbreviation disambiguation in biomedical documents has been studied by various researchers using supervised machine learning algorithms (Liu et al, 2004;Gaudan et al, 2005;Yu et al, 2006;Ucgun et al, 2006;Stevenson et al, 2009). However, the performance of these supervised methods mainly depends on a large amount of labeled data which is extremely difficult to obtain for our task since intensive care medicine texts are very rare resources in clinical domain due to the high cost of de-identification and annotation.…”

Section: Related Workmentioning

confidence: 99%

Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion

Liu

Mathews

et al. 2015

Proceedings of BioNLP 15

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In the medical domain, identifying and expanding abbreviations in clinical texts is a vital task for both better human and machine understanding. It is a challenging task because many abbreviations are ambiguous especially for intensive care medicine texts, in which phrase abbreviations are frequently used. Besides the fact that there is no universal dictionary of clinical abbreviations and no universal rules for abbreviation writing, such texts are difficult to acquire, expensive to annotate and even sometimes, confusing to domain experts. This paper proposes a novel and effective approach -exploiting taskoriented resources to learn word embeddings for expanding abbreviations in clinical notes. We achieved 82.27% accuracy, close to expert human performance.

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Disambiguation of biomedical abbreviations

Cited by 41 publications

References 21 publications

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

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

eFIP: A Tool for Mining Functional Impact of Phosphorylation from Literature

Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion

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