A Survey of Word-sense Disambiguation Effective Techniques and Methods for Indian Languages <s> C. Punjabi <s> To solve the knowledge bottleneck problem, active learning has been widely used for its ability to automatically select the most informative unlabeled examples for human annotation. One of the key enabling techniques of active learning is uncertainty sampling, which uses one classifier to identify unlabeled examples with the least confidence. Uncertainty sampling often presents problems when outliers are selected. To solve the outlier problem, this paper presents two techniques, sampling by uncertainty and density (SUD) and density-based re-ranking. Both techniques prefer not only the most informative example in terms of uncertainty criterion, but also the most representative example in terms of density criterion. Experimental results of active learning for word sense disambiguation and text classification tasks using six real-world evaluation data sets demonstrate the effectiveness of the proposed methods. <s> BIB001 </s> A Survey of Word-sense Disambiguation Effective Techniques and Methods for Indian Languages <s> C. Punjabi <s> In this paper we concentrate on the resolution of the lexical ambiguity that arises when a given word has several different meanings. This specific task is commonly referred to as word sense disambiguation (WSD). The task of WSD consists of assigning the correct sense to words using an electronic dictionary as the source of word definitions. We present two WSD methods based on two main methodological approaches in this research area: a knowledge-based method and a corpus-based method. Our hypothesis is that word-sense disambiguation requires several knowledge sources in order to solve the semantic ambiguity of the words. These sources can be of different kinds-- for example, syntagmatic, paradigmatic or statistical information. Our approach combines various sources of knowledge, through combinations of the two WSD methods mentioned above. Mainly, the paper concentrates on how to combine these methods and sources of information in order to achieve good results in the disambiguation. Finally, this paper presents a comprehensive study and experimental work on evaluation of the methods and their combinations. <s> BIB002
The Punjabi language is morphologically rich. Rakesh and Ravinder BIB002 have given the WSD algorithm for removing ambiguity from the text document.WSD algorithm used by authors is Modified Lesk's Algorithm. There are two hypothesis that underly this approach. The first is that words appears together in a sentence can be disambiguated by assigning to them the senses that are most closely related to their neighboring words. The second hypothesis is that related senses can be identified by finding overlapping words in their definitions Choose the Minimum Depth Sense Figure 5 System Design using conceptual density from BIB001 