A Survey of Word-sense Disambiguation Effective Techniques and Methods for Indian Languages <s> B. Extended Word Net <s> 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. <s> BIB001 </s> A Survey of Word-sense Disambiguation Effective Techniques and Methods for Indian Languages <s> B. Extended Word Net <s> 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. <s> BIB002
In the Lesk algorithm  , word to disambiguate is given, the dictionary definition or gloss of each of its senses is compared to the glosses of every other word in the phrase. A word is assigned that sense whose gloss shares the largest number of words in common with the glosses of the other words. The algorithm begins a new for each word and does not utilize the senses it previously assigned. A version of Lesk algorithm in combination with WordNet has been reported for achieving good word sense disambiguation results BIB001 . In their work, different types of relationships in WordNet have been experimented with. It showed that the best results are obtained when concatenating the descriptions of word senses with the glosses of its first and second-levels hypernyms. This algorithm is used by Naskar and Bandyopadhyay  , in which they have used the Word Net lexical database, because it contains different types of relationships between words. They proposed a global approach instead of local approach where all the words in the context window are simultaneously disambiguated in a bid to get the best combination of senses for all the words in the window instead of only the target word. The Lesk algorithm only work for short phrases. But the algorithm proposed by  takes the entire sentence under consideration. The gloss bag is constructed for every sense of every word in the sentence. The gloss-bag is constructed from the POS and sense tagged glosses of synsets, obtained from the Extended Word Net. Once, the gloss-bag creation process is over, the comparison process starts. 'barrage' BIB002 words match in part-of speech as well, a score is assigned to both the words: the word being matched (W i ) and the word whose gloss-bag contains the match (W j ). This matching event indicates mutual confidence towards each other, so both words are rewarded for this event. Two two-dimensional vectors are maintained: sense_vote for the word in context, and sense_score for the word in gloss-bag. Once all the comparisons have been made, sense_vote value is added with the sense_score linearly value for each sense of every word to arrive at the combination score for this word-sense pair. Finally, for any word in the context, the value of sense index that maximizes this sum is declared the assigned sense for this particular word. Knowledge base used by Naskar and Bandyopadhyay  was first 10 Semcor2.0 files. Another approach of knowledge based Disambiguation is using Word Net domains which is used for disambiguate nouns. It follows the unsupervised approach to word sense disambiguation   . Domain is defined as set of words which contain the words with the semantic relation. This algorithm use 3 bags for solving ambiguity. Bag 3 contains the target word which we need to disambiguate and bag 3 is compared with bag 1 and bag 2. First, Domain of the word is interpreted and then the sense in that domain is the sense of the target word in bag 3. Precision of the algorithm was 85.9%, and the recall was calculated 62.1%.