Co-occurrence graphs for word sense disambiguation in the biomedical domain

Word sense disambiguation is the problem of selecting a sense for a word from a set of predefined possibilities. This is a significant problem in the biomedical domain where a single word may be used to describe a gene, protein, or abbreviation. In this paper, we evaluate SENSATIONAL, a novel unsupervised WSD technique, in comparison with two popular learning algorithms: support vector machines (SVM) and K-means. Based on the accuracy measure, our results show that SENSATIONAL outperforms SVM and K-means by 2% and 17%, respectively. In addition, we develop a polysemy-based search engine and an experimental visualization application that utilizes SENSATIONAL’s clustering technique.

Download Full-text

Analysis of the methods of word sense disambiguation in the biomedical domain

Doklady BGUIR ◽

10.35596/1729-7648-2019-123-5-60-65 ◽

2019 ◽

pp. 60-65

Author(s):

A. V. Pashuk ◽

A. B. Gurinovich ◽

N. A. Volorova ◽

A. P. Kuznetsov

Keyword(s):

Word Sense Disambiguation ◽

Biomedical Domain ◽

Word Sense ◽

Sense Disambiguation

Download Full-text

A Learning-Based Approach for Biomedical Word Sense Disambiguation

The Scientific World JOURNAL ◽

10.1100/2012/949247 ◽

2012 ◽

Vol 2012 ◽

pp. 1-8 ◽

Cited By ~ 5

Author(s):

Hisham Al-Mubaid ◽

Sandeep Gungu

Keyword(s):

Research Effort ◽

Word Sense Disambiguation ◽

Interaction Model ◽

Biomedical Domain ◽

Word Sense ◽

Text Annotation ◽

Ambiguous Words ◽

Sense Disambiguation ◽

Supervised Methods ◽

Automatic Text

In the biomedical domain, word sense ambiguity is a widely spread problem with bioinformatics research effort devoted to it being not commensurate and allowing for more development. This paper presents and evaluates a learning-based approach for sense disambiguation within the biomedical domain. The main limitation with supervised methods is the need for a corpus of manually disambiguated instances of the ambiguous words. However, the advances in automatic text annotation and tagging techniques with the help of the plethora of knowledge sources like ontologies and text literature in the biomedical domain will help lessen this limitation. The proposed method utilizes the interaction model (mutual information) between the context words and the senses of the target word to induce reliable learning models for sense disambiguation. The method has been evaluated with the benchmark dataset NLM-WSD with various settings and in biomedical entity species disambiguation. The evaluation results showed that the approach is very competitive and outperforms recently reported results of other published techniques.

Download Full-text

A Comparative Study of an Unsupervised Word Sense Disambiguation Approach

Applied Natural Language Processing ◽

10.4018/978-1-60960-741-8.ch024 ◽

2012 ◽

pp. 414-424

Author(s):

Wei Xiong ◽

Min Song ◽

Lori deVersterre

Keyword(s):

Support Vector Machines ◽

Search Engine ◽

Word Sense Disambiguation ◽

Support Vector ◽

Biomedical Domain ◽

Word Sense ◽

Accuracy Measure ◽

Vector Machines ◽

Sense Disambiguation ◽

Visualization Application

Word sense disambiguation is the problem of selecting a sense for a word from a set of predefined possibilities. This is a significant problem in the biomedical domain where a single word may be used to describe a gene, protein, or abbreviation. In this paper, we evaluate SENSATIONAL, a novel unsupervised WSD technique, in comparison with two popular learning algorithms: support vector machines (SVM) and K-means. Based on the accuracy measure, our results show that SENSATIONAL outperforms SVM and K-means by 2% and 17%, respectively. In addition, we develop a polysemy-based search engine and an experimental visualization application that utilizes SENSATIONAL’s clustering technique.

Download Full-text

Word Sense Disambiguation in Biomedical Applications

Information Retrieval in Biomedicine ◽

10.4018/978-1-60566-274-9.ch008 ◽

2010 ◽

pp. 142-161 ◽

Cited By ~ 1

Author(s):

Torsten Schiemann ◽

Ulf Leser ◽

Jörg Hakenberg

Keyword(s):

Word Sense Disambiguation ◽

Ambiguous Word ◽

Biomedical Domain ◽

Word Sense ◽

Common Phenomenon ◽

Knowledge Framework ◽

Sense Disambiguation ◽

Potential Sources ◽

Training Examples ◽

Difficult Part

Ambiguity is a common phenomenon in text, especially in the biomedical domain. For instance, it is frequently the case that a gene, a protein encoded by the gene, and a disease associated with the protein share the same name. Resolving this problem, that is, assigning to an ambiguous word in a given context its correct meaning is called word sense disambiguation (WSD). It is a pre-requisite for associating entities in text to external identifiers and thus to put the results from text mining into a larger knowledge framework. In this chapter, we introduce the WSD problem and sketch general approaches for solving it. The authors then describe in detail the results of a study in WSD using classification. For each sense of an ambiguous term, they collected a large number of exemplary texts automatically and used them to train an SVM-based classifier. This method reaches a median success rate of 97%. The authors also provide an analysis of potential sources and methods to obtain training examples, which proved to be the most difficult part of this study.

Download Full-text

An Improved Supervised Word Sense Disambiguation Methods in the Biomedical Domain

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.241-244.3103 ◽

2012 ◽

Vol 241-244 ◽

pp. 3103-3106

Author(s):

Jia Cong He ◽

Kai Ren ◽

Wei Jie Yu

Keyword(s):

Word Sense Disambiguation ◽

Biomedical Domain ◽

Word Sense ◽

Improved Method ◽

Sense Disambiguation ◽

Initial Dataset

The effectiveness of supervised word sense disambiguation approaches depend in part on the availability of initial dataset. In the paper, we use an improved method based on the simple K-means clustering function, the result indicates the improvement after using the method. The whole experiments are running on the NLM WSD set, which is emphasizing on the biomedical domain.

Download Full-text