Word sense disambiguation: a survey <s> INTRODUCTION <s> Classifier combination is an effective and broadly useful method of improving system performance. This article investigates in depth a large number of both well-established and novel classifier combination approaches for the word sense disambiguation task, studied over a diverse classifier pool which includes feature-enhanced Naive Bayes, Cosine, Decision List, Transformation-based Learning and MMVC classifiers. Each classifier has access to the same rich feature space, comprised of distance weighted bag-of-lemmas, local ngram context and specific syntactic relations, such as Verb-Object and Noun-Modifier. This study examines several key issues in system combination for the word sense disambiguation task, ranging from algorithmic structure to parameter estimation. Experiments using the standard SENSEVAL2 lexical-sample data sets in four languages (English, Spanish, Swedish and Basque) demonstrate that the combination system obtains a significantly lower error rate when compared with other systems participating in the SENSEVAL2 exercise, yielding state-of-the-art performance on these data sets. <s> BIB001 </s> Word sense disambiguation: a survey <s> INTRODUCTION <s> In supervised methods of word sense disambiguation, sense tagged samples for training classifiers are needed. Since sense tagging for ambiguous words is expensive and labor intensive, it is worth looking for some reasonable substitutes. We suggest a kind of substitute for Chinese sense tagged data, which is called pseudo training data. The suggestion is based on a linguistic phenomenon in Chinese, which some multicharacter-words inherit only one sense and some syntactical features from ambiguous one-character-words. Data derived from unambiguous multicharacter-words are employed as pseudo training data. Pseudo training data have an advantage of being able to be collected automatically. Our experiments show that classifiers trained by not too much pseudo training data outperform classifiers trained by small quantities of sense tagged samples for Chinese ambiguous word senses. Further experiments show combination of a small set of tagged data and a large quantities pseudo training data is a more promising way to word sense disambiguation. <s> BIB002 </s> Word sense disambiguation: a survey <s> INTRODUCTION <s> Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence. We introduce the reader to the motivations for solving the ambiguity of words and provide a description of the task. We overview supervised, unsupervised, and knowledge-based approaches. The assessment of WSD systems is discussed in the context of the Senseval/Semeval campaigns, aiming at the objective evaluation of systems participating in several different disambiguation tasks. Finally, applications, open problems, and future directions are discussed. <s> BIB003 </s> Word sense disambiguation: a survey <s> INTRODUCTION <s> One of the major issues in the process of machine translation is word sense disambiguation (WSD), which is defined as choosing the correct meaning of a multi-meaning word. Supervised learning methods are usually used to solve this problem. The disambiguation task is carried out using the statistics of the translated documents (as training data) or dual corpora of source and target languages. In this paper we present a supervised learning method for WSD, which is based on Cosine Similarity. As the first step, we extract two sets of features; the set of words that have occurred frequently in the text and the set of words surrounding the ambiguous word. We will present the results of evaluating the proposed schemes and illustrate the effect of weighting strategies proposed. The results are promising compared to the methods existing in the literature. In corpora-based Translation methods translations are generated on the basis of statistical or probabilistic models whose parameters are extracted from the analysis of a bilingual corpus. Statistical translation is based on the study of frequencies of various linguistic units, including words, lexemes, morphemes, letters, etc., in a sample corpus in order to calculate a set of probabilities, so that various linguistic problems such as ambiguity can be solved. In This paper, we present a WSD approach that is based on inner product of vectors algorithm. The proposed scheme is a supervised approach in which sense-tagged data is used to train the classifier. <s> BIB004
In all the major languages around the world, there are a lot of words which denote meanings in different contexts. Word Sense Disambiguation  BIB001 BIB004 BIB002 BIB003 is a technique to find the exact sense of an ambiguous word in a particular context. For example, an English word 'bank' may have different senses as "financial institution", "river side", "reservoir" etc. Such words with multiple senses are called ambiguous words and the process of finding the exact sense of an ambiguous word for a particular context is called Word Sense Disambiguation. A normal human being has an inborn capability to differentiate the multiple senses of an ambiguous word in a particular context, but the machines run only according to the instructions. So, different rules are fed to the system to execute a particular task. WSD approaches are categorized mainly into three types, Knowledge-based, Supervised and Unsupervised methods, which is described in detail later. The organization of the paper is as follows: section 2 depicts the Brief History of WSD Research; in section 3, Applications of WSD is discussed; different WSD Approaches are discussed in section 4; section 5 depicts the State-of-the-Art Performance; section 6 represents the Comparison of different types of algorithms; An overview of WSD for Indian Languages is described in section 7; and we conclude the discussion in section 8.