A b s t r a c t Objective: The aim of this study was to investigate relations among different aspects in supervised word sense disambiguation (WSD; supervised machine learning for disambiguating the sense of a term in a context) and compare supervised WSD in the biomedical domain with that in the general English domain. The study involves three data sets (a biomedical abbreviation data set, a general biomedical term data set, and a general English data set). The authors implemented three machine-learning algorithms, including (1) naïve Bayes (NBL) and decision lists (TDLL), (2) their adaptation of decision lists (ODLL), and (3) their mixed supervised learning (MSL). There were six feature representations (various combinations of collocations, bag of words, oriented bag of words, etc.) and five window sizes (2, 4, 6, 8, and 10). Results: Supervised WSD is suitable only when there are enough sense-tagged instances with at least a few dozens of instances for each sense. Collocations combined with neighboring words are appropriate selections for the context. For terms with unrelated biomedical senses, a large window size such as the whole paragraph should be used, while for general English words a moderate window size between 4 and 10 should be used. The performance of the authors' implementation of decision list classifiers for abbreviations was better than that of traditional decision list classifiers. However, the opposite held for the other two sets. Also, the authors' mixed supervised learning was stable and generally better than others for all sets. Conclusion: From this study, it was found that different aspects of supervised WSD depend on each other. The experiment method presented in the study can be used to select the best supervised WSD classifier for each ambiguous term. Word sense disambiguation (WSD) is the problem of tagging the appropriate sense of a given word in a context. Resolving sense ambiguity is one of the most important problems in natural language processing (NLP) and is essential for any kind of text-understanding task such as information extraction, information retrieval, or message understanding. Despite the wide range of approaches investigated, including expert rules and supervised or unsupervised machinelearning techniques, currently there is no large-scale, broadcoverage, and highly accurate WSD system. One of the encouraging approaches to WSD is supervised machine learning. 2 Given an ambiguous word W, a supervised WSD classifier is obtained by applying supervised machine learning on a collection of sense-tagged instances of W, called a sense-tagged corpus of W STC(W), in which the sense of W in each instance has been tagged. Supervised approaches tend to achieve better performance than other WSD approaches. 4, 5 However, supervised WSD suffers from the lack of a large, broad-coverage, sense-tagged corpus. Currently, there are two lines of research tackling this problem 6,7 : (1) design efficient sampling methods to lower the cost of sense tagging or (2) use a machine-readable dictionary (MRD) and a large collection of raw text to obtain a raw sense-tagged corpus. In several previous reports, 8, 9 we have shown that methods based on MRD could be used to obtain a large, sense-tagged corpus for ambiguous biomedical abbreviations. We also did a comparison study in one report, 8 but it was limited (with one data set specialized in the medical reports domain, a couple of existing machine-learning algorithms, and a few feature representations that did not include collocations). The main focus of that report was to propose an unsupervised WSD method and not to compare different supervised WSD classifiers. Results showed that further exploration of supervised WSD was warranted. In this report, we investigated the relations among different aspects of supervised WSD and compared WSD in the biomedical domain with that in the general English domain. Here, we first present background and related work about supervised WSD. We then describe our experiment on supervised WSD involving several aspects: data set, feature Affiliations of the authors:
REF studied various sizes of immediate contexts to the right and to the left of the ambiguous word for training of machine learning algorithms that demonstrated high accuracy in general English word sense disambiguation, namely, naive Bayes, decision list, and a combination of a naive Bayes and an instance-based classifier.
A multi-aspect comparison study of supervised word sense disambiguation
Journal of the American Medical Informatics Association : JAMIA
Journal of the American Medical Informatics Association : JAMIA
Computer Science Medicine