Abstract
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.
Highlights
Word sense disambiguation is the task of determining the correct sense of a given word in a given context
In the biomedical texts, the term “blood pressure” has three possible senses according to the Unified Medical Language System (UMLS) [3] as follows: organism function, diagnostic procedure, and laboratory or test result
If this term blood pressure is found in a medical text, the reader has to manually judge and determines which one of these three senses is intended in that text
Summary
Word sense disambiguation is the task of determining the correct sense of a given word in a given context. In the biomedical texts, the term “blood pressure” has three possible senses according to the Unified Medical Language System (UMLS) [3] as follows: organism function, diagnostic procedure, and laboratory or test result. If this term blood pressure is found in a medical text, the reader has to manually judge and determines which one of these three senses is intended in that text. Word sense disambiguation contributes in many important applications including the text mining, information extraction, and information retrieval systems [1, 2, 4]. It is considered a key component in most intelligent knowledge discovery and text mining applications
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