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Hindi language is written and spoken by majority of people in India. Like other natural languages, Hindi is also an ambiguous language which creates obstacle in usage of information technology properly. To use Hindi language efficiently and effectively on web, we require a tool to remove ambiguity from a single word, or from all words, called word sense disambiguation (WSD). In this paper we introduce...
This paper presents a modified Adapted Lesk algorithm for word sense disambiguation in Nepali language. We included the synset, gloss, example and hypernym of the context words to form the final collection of context words. The context window contains all the words from the whole sentence except articles, prepositions and pronouns. The collection of words for each sense of a target word is also formed...
Traditional text categorization methods only deal with the content of the documents and use some statistic based metrics to represent the documents. The representation is then used by a machine learning approach to determine the document class. In this picture, the meaning of the document is missing. In order to add meaning into the text categorization process, we start with using part-of-speech tagging...
Word sense disambiguation is an important intermediate stage for many natural language processing applications, especially transformation from Cyrillic into Mongolian script. A word sense could be disambiguated by other words in the context as nouns, verbs used with the word. In this research, we have analyzed the result of an experiment on a word disambiguation system for Mongolian language based...
Word Sense Disambiguation (WSD) is the task of choosing the most appropriate sense of a word having multiple senses in a given context. Collocational features acquired from the words in neighborship with the ambiguous word are one of the important knowledge sources in this area. This paper explores the effective sets of collocational features in Turkish in order to obtain better Turkish WSD systems...
This paper investigates the effects of stemming, stop word removal and size of context window on Hindi word sense disambiguation. The evaluation has been made on a manually created sense tagged corpus consisting of Hindi words (nouns). The sense definition has been obtained from Hindi WordNet, which is an important lexical resource for Hindi language developed at IIT Bombay. The maximum observed precision...
In general, different levels of knowledge are used for disambiguation. In this paper, only three knowledge features or sources (trigram) are used to achieve the word sense disambiguation. Case based approach is applied for the disambiguation process. Cases are nothing but the refined form of words collected from Semcor, used for deriving the sense of the ambiguous input word. All possible Part of...
Word Sense Disambiguation is one of the essential tasks in the Natural Language Processing that it used to identify the correct sense of words. There are many approaches for Word Sense Disambiguation that in this paper proposes an algorithm based on weighted graph which has few parameters and does not require sense-annotated data for training. Also we used standard data sets to evaluate the algorithm.
In computational linguistics, word sense disambiguation is an open problem and is important in various aspects of natural language processing. However, the traditional methods using case frames and semantic primitives are not effective for solving context ambiguities that require information beyond sentences. This paper presents a new method of solving context ambiguities using a field association...
This article studies different aspect of a new approach for resolving lexical ambiguities using statistical information gained from a monolingual corpus. The proposed approach resolves the problem of target word selection in an machine translation system. This Method is an unsupervised graph-based approach which uses a bilingual dictionary to find all possible translations of each ambiguous word in...
Word Sense Disambiguation (WSD) is the task of selecting the meaning of a word based on the context in which the word occurs. The principal statistical WSD approaches are supervised and unsupervised learning. The Lesk method is an example of unsupervised disambiguation. We present a measure for sense assignment useful for the simple Lesk algorithm. We use word co-occurrences of the gloss and the context,...
Word sense disambiguation (WSD) is a traditional AI-hard problem. An improvement of WSD would have a significant impact on applications such as knowledge acquisition, text mining, information extraction, etc. Lexical chain holds a set of semantically related words of a text and provides an effective way for WSD, but existing lexical chain systems have inaccuracies in WSD for lacking a weighting scheme...
Word Sense Disambiguation in text is still a difficult problem as the best supervised methods require laborious and costly manual preparation of training data. On the other hand, the unsupervised methods express significantly lower accuracy and produce results that are not satisfying for many application. The goal of this work is to develop a model of Word Sense Disambiguation which minimises the...
Clustering semantically related terms is crucial for many applications such as document categorization, and word sense disambiguation. However, automatically identifying semantically similar terms is challenging. We present a novel approach for automatically determining the degree of relatedness between terms to facilitate their subsequent clustering. Using the analogy of ensemble classifiers in machine...
Natural languages are typically replete with homographs, words which have more than one meaning. Consequently, machine understanding of natural language sentences sometimes suffers from certain ambiguities in getting the correct sense of a word in a given sentence. In this work we present a trainable model for word sense disambiguation (WSD) for resolving this ambiguity. The proposed model applies...
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