Improvement in Word Sense Disambiguation by Introducing Enhancements in English WordNet Structure?

In computational linguistics, word-sense disambiguation is a major problem of processing a natural language, which rules the process of automatically finding out the intended sense of a particular word in a sentence. There is an algorithm introduced by Michael Lesk's in 1986. This algorithm is based on two assumptions. First, when two words are used in close proximity in a sentence, they must be talking of a related topic and second, if one sense each of the two words can be used to talk of the same topic, then their dictionary definitions must use some common words. For example, when the words ”pine cone” occur together, they are talking of ”evergreen trees”, and indeed one meaning each of these two words has the words ”evergreen” and ”tree” in their definitions. Thus we can disambiguate neighbouring words in a sentence by comparing their definitions and picking those senses whose definitions have the most number of common words. In this paper we introduce a new WordNet based on old existing WordNet with some additional features that may enhance the efficiency of knowledge-based contextual overlap WSD algorithms when they are used on wordnets. The proposed scheme has been tested on a number of heterogeneous sentences with word using Lesk algorithm. The Lesk algorithm is used to return the sense identifiers for the words used to classify the text files by looking up the senses of a word in a Knowledge-Base similar to the English WordNet (enriched with more informative columns or fields for each synset of the English WordNet database), so as to greatly increase the chances of contextual overlap, thereby resulting in high accuracy of proper sense or context identification of the words. The WSD results and accuracies, obtained using the proposed new WordNet, have been compared with the results obtained using existing WordNet. Experimental results show that our WordNet performs much better than the existing WordNet. The technique will thus help the users much better in Machine translation, which has been identified as one of the most challenging areas in natural language processing.