In today’s digital world language technology has gained importance. Several software, have been developed and are available in the field of computational linguistics. Such tools play a crucial role in making classical language texts easily …
We present a WordNet like structured resource for slang words and neologisms on the internet. The dynamism of language is often an indication that current language technology tools trained on today's data, may not be able to process the language in …
The task of Word Sense Disambiguation (WSD) incorporates in its definition the role of ‘context’. We present our work on the development of a tool which allows for automatic acquisition and ranking of ‘context clues’ for WSD. These clue words are …
Word Sense Disambiguation (WSD) approaches have reported good accuracies in recent years. However, these approaches can be classified as weak AI systems. According to the classical definition, a strong AI based WSD system should perform the task of …
Does context help determine sense? This question might seem frivolous, even preposterous to anybody sensible. However, our long time research on Word Sense Disambiguation (WSD) shows that in almost all disambiguation algorithms, the sense …
Current state-of-the-art Word Sense Disambiguation (WSD) algorithms are mostly supervised and use the P (Sense|Word) statistic for annotation. This P (Sense|Word) statistic is obtained after training the model on an annotated corpus. The performance …