Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Word Sense Disambiguation (WSD) is the task of choosing the right sense of an ambiguous word given a context. Using Naive Bayesian (NB) classifiers is known as one of the best methods for supervised approaches for WSD (Mooney, 1996; Pedersen, 2000), and this model usually uses only a topic context represented by unordered words in a large context. ,  show that by adding more rich knowledge, represented by ordered words in a local context and collocations, the NB classifier can achieve higher accuracy in comparison with the best previously published results. The features re chosen using a forward sequential selection algorithm. Our experiments obtained 92.3% accuracy for four common test words (interest, line, hard, serve).  also tested on a large dataset, the DSO corpus, and obtained accuracies of 66.4% for verbs and 72.7% for nouns.
['Cuong Anh Le', 'Akira Shimazu']
I am writing a research paper on a idea High WSD Accuracy Using Naive Bayesian Classifier with Rich Features
[{"from": "human", "value": "I am writing a research paper on a idea High WSD Accuracy Using Naive Bayesian Classifier with Rich Features"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Word Sense Disambiguation (WSD) is the task of choosing the right sense of an ambiguous word given a context. Using Naive Bayesian (NB) classifiers is known as one of the best methods for supervised approaches for WSD (Mooney, 1996; Pedersen, 2000), and this model usually uses only a topic context represented by unordered words in a large context. ,  show that by adding more rich knowledge, represented by ordered words in a local context and collocations, the NB classifier can achieve higher accuracy in comparison with the best previously published results. The features re chosen using a forward sequential selection algorithm. Our experiments obtained 92.3% accuracy for four common test words (interest, line, hard, serve).  also tested on a large dataset, the DSO corpus, and obtained accuracies of 66.4% for verbs and 72.7% for nouns."}]