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The paper presents a study on word sense disambiguation (WSD) in Bengali, one of the less resourced Indian languages. The overall work is carried out in two sequential phases. In the first phase, four well-known approaches, which are often applied for sense disambiguation of words, are studied using the traditional methods. In the course of application, suitable modifications are made as well as implemented for eliciting desired results. In the second stage, a combined approach is proposed based on the results obtained from initial experiments. Within 'supervised module' the four commonly used methods, namely, the Decision Tree (DT) method, Support Vector Machine (SVM) method, Artificial Neural Network (ANN) method, and the Naïve Bayes (NB) method are used at the baseline for the purpose of classification of senses. These baseline strategies produced 63.84%, 76.9%, 76.23%, and 80.23% accurate results, respectively, when these methods are tested on 13 mostly used Bengali ambiguous words retrieved from a Bengali text corpus. Next, two major modifications are applied on these baseline strategies to increase the level of accuracy: (a) incorporation of Lemmatization process in the system (that produces 68.30%, 79%, 78.23%, and 82.30% accurate results, respectively), and (b) operation of Bootstrapping on the systems (including lemmatization feature), which produces 70.92%, 79.15%, 79.53%, and 83% accuracy, respectively. Next, in a knowledge-based method, the traditional Lesk algorithm is implemented at the baseline which produces 31% accurate result in sense disambiguation. This strategy is further modified by Context Expansion (CE) method in the sentences using the Bengali WordNet to produce 75% accuracy. Within 'unsupervised module', the baseline strategy produced a 36.2% accurate result in sense disambiguation task. To enhance the level of performance, two modifications are adopted over this baseline strategy: (a) Principal Component Analysis (PCA) over the feature vector, which produces 51.2% accuracy in WSD task, and (b) Context Expansion of the sentences using the Bengali WordNet with PCA, which produces 61% accuracy in sense disambiguation task. Finally, a combined approach is adopted after considering all the effective aspects of the three methods, and it produces the highest level accuracy (92%) in the task of sense disambiguation. [ABSTRACT FROM AUTHOR]

An attempt is made in this paper to report how a supervised methodology has been adopted for the task of Word Sense Disambiguation (WSD) in Bengali with necessary modifications. At the initial stage, four commonly used supervised methods, Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Naïve Bayes (NB), are developed at the baseline. These algorithms are applied individually on a data set of 13 most frequently used Bengali ambiguous words. On experimental basis, the baseline strategy is modified with two extensions: (a) inclusion of lemmatization process into the system and (b) bootstrapping of the operational process. As a result, the levels of accuracy of the baseline methods are slightly improved, which is a positive signal for the whole process of disambiguation as it opens scope for further modification of the existing method for better result. In this experiment, the data sets are prepared from the Bengali corpus, developed in the Technology Development for Indian Languages (TDIL) project of the Government of India and from the Bengali WordNet, which is developed at the Indian Statistical Institute, Kolkata. The paper reports the challenges and pitfalls of the work that have been closely observed during the experiment. [ABSTRACT FROM AUTHOR]