In this paper, the effect of different windowing schemes on word sense disambiguation accuracy is presented. Turkish Lexical SampleDataset has been used in the experiments. We took the samples of ambiguous verbs and nouns ...

This paper presents an exploration and evaluation of a diverse set of features that influence word-sense disambiguation (WSD) performance. WSD has the potential to improve many natural language processing (NLP) tasks as ...

Word Sense Disambiguation (WSD) has become even more important research area in recent years with the widespread usage of Natural Language Processing (NLP) applications. WSD task has two variants: “Lexical Sample” and “All ...

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 ...

Feature selection in Word Sense Disambiguation (WSD) is as important as the selection of algorithm to remove sense ambiguity. Bag-of-word (BoW) features comprise the information of neighbors around the ambiguous target ...