A Survey of Word-sense Disambiguation Effective Techniques and Methods for Indian Languages
---
paper_title: A Context Expansion Method for Supervised Word Sense Disambiguation

paper_content:

Feature sparseness is one of the main causes for Word Sense Disambiguation (WSD) systems to fail, as it increases the probability of incorrect predictions. In this work, we present a WSD method to overcome this problem by using an automatically-created thesaurus to append related words to a specific context, in order to improve the effectiveness of candidate selection for an ambiguous word. We treat the context as a vector of words taken from sentences, and expand it with words from the thesaurus according to their mutual relatedness. Our results suggest that the method performs disambiguation with high precision.
---
paper_title: An improved unsupervised learning probabilistic model of word sense disambiguation

paper_content:

Unsupervised learning can address the general limitation of supervised learning that sense-tagged text is not available for most domains and is expensive to create. However, the existing unsupervised learning probabilistic models are computationally expensive and convergence slowly because of large numbers and random initialization of model parameters. This paper reduces the noise jamming and the dimensionality of the models by using proposed feature selection and initial parameter estimation. Experimental result shows the accuracy and efficiency of the proposed probabilistic model are obviously improved.
---
paper_title: HyperLex: Lexical Cartography for Information Retrieval

paper_content:

Abstract This article describes an algorithm called HyperLex that is capable of automatically determining word uses in a textbase without recourse to a dictionary. The algorithm makes use of the specific properties of word cooccurrence graphs, which are shown as having “small world” properties. Unlike earlier dictionary-free methods based on word vectors, it can isolate highly infrequent uses (as rare as 1% of all occurrences) by detecting “hubs” and high-density components in the cooccurrence graphs. The algorithm is applied here to information retrieval on the Web, using a set of highly ambiguous test words. An evaluation of the algorithm showed that it only omitted a very small number of relevant uses. In addition, HyperLex offers automatic tagging of word uses in context with excellent precision (97%, compared to 73% for baseline tagging, with an 82% recall rate). Remarkably good precision (96%) was also achieved on a selection of the 25 most relevant pages for each use (including highly infrequent ones). Finally, HyperLex is combined with a graphic display technique that allows the user to navigate visually through the lexicon and explore the various domains detected for each word use.
---
paper_title: An improved unsupervised learning probabilistic model of word sense disambiguation

paper_content:

Unsupervised learning can address the general limitation of supervised learning that sense-tagged text is not available for most domains and is expensive to create. However, the existing unsupervised learning probabilistic models are computationally expensive and convergence slowly because of large numbers and random initialization of model parameters. This paper reduces the noise jamming and the dimensionality of the models by using proposed feature selection and initial parameter estimation. Experimental result shows the accuracy and efficiency of the proposed probabilistic model are obviously improved.
---
paper_title: A Context Expansion Method for Supervised Word Sense Disambiguation

paper_content:

Feature sparseness is one of the main causes for Word Sense Disambiguation (WSD) systems to fail, as it increases the probability of incorrect predictions. In this work, we present a WSD method to overcome this problem by using an automatically-created thesaurus to append related words to a specific context, in order to improve the effectiveness of candidate selection for an ambiguous word. We treat the context as a vector of words taken from sentences, and expand it with words from the thesaurus according to their mutual relatedness. Our results suggest that the method performs disambiguation with high precision.
---
paper_title: The SemEval-2007 WePS Evaluation: Establishing a benchmark for the Web People Search Task

paper_content:

This paper presents the task definition, resources, participation, and comparative results for the Web People Search task, which was organized as part of the SemEval-2007 evaluation exercise. This task consists of clustering a set of documents that mention an ambiguous person name according to the actual entities referred to using that name.
---
paper_title: Active Learning With Sampling by Uncertainty and Density for Data Annotations

paper_content:

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.
---
paper_title: Combining Knowledge- and Corpus-based Word-Sense-Disambiguation Methods

paper_content:

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.
---
paper_title: Active Learning With Sampling by Uncertainty and Density for Data Annotations

paper_content:

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.
---

Title: A Survey of Word-sense Disambiguation Effective Techniques and Methods for Indian Languages

Section 1: INTRODUCTION
Description 1: Write an introduction about the concept of word sense disambiguation (WSD), its importance in natural language processing, the main approaches, and its applications.

Section 2: WSD APPROACHES
Description 2: Discuss the various approaches for WSD, including knowledge-based and machine-learning-based approaches, and provide insights into each approach.

Section 3: Machine Learning Based Approach
Description 3: Detail the machine learning-based approach for WSD, including supervised, unsupervised, and semi-supervised techniques.

Section 4: Dictionary Based Approach
Description 4: Describe the dictionary-based approach for WSD, including specific methods such as HyperLex and extended Word Net.

Section 5: Improved Unsupervised Learning Probabilistic Model
Description 5: Provide an overview of the improved unsupervised learning probabilistic model for WSD, including its algorithm and parameter estimation.

Section 6: Genetic Algorithm for WSD
Description 6: Explain the genetic algorithm approach for WSD, specifically its application to the Arabian language.

Section 7: Applications of WSD
Description 7: Discuss the various applications of WSD, including information extraction, information retrieval, machine translation, text processing, and speech processing.

Section 8: WSD FOR INDIAN LANGUAGES
Description 8: Discuss the work done for WSD in various Indian languages including Manipuri, Malayalam, Tamil, Kannada, Hindi, and Punjabi.

Section 9: Manipuri
Description 9: Provide a detailed description of WSD work done in Manipuri, including the architecture and methodology used.

Section 10: Malayalam
Description 10: Describe the efforts and methodologies for WSD in Malayalam, including specific algorithms and approaches used.

Section 11: Punjabi
Description 11: Outline the WSD techniques used for Punjabi, focusing on specific algorithms like the modified Lesk’s algorithm.