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A Brief Overview of Natural Language Processing and Artificial Intelligence
Published in Brojo Kishore Mishra, Raghvendra Kumar, Natural Language Processing in Artificial Intelligence, 2020
Sushree Bibhuprada B. Priyadarshini, Amiya Bhusan Bagjadab, Brojo Kishore Mishra
Unsupervised learning represents the greatest ultimatum for WSD investigators. The assumption is that similar senses prevail in similar types of contexts, and therefore, senses can get induced from the text by considering word occurrences using the similarity in the context. Further, new occurrences of the word can get classified into the closest induced clusters/senses. Further, word sense induction strategies can be tested and compared within any application prospect. Further, word sense induction improves [8] Web search outcome clustering by enhancing the quality of resultant clusters conjointly with the degree diversification of result lists.
Representing word meaning in context via lexical substitutes
Published in Automatika, 2021
While lexical substitution (LS) intuitively appears to be a sensible approach to representing word meaning in context, it is by no means evident how it relates to sense-based representation. However, determining the correspondence between substitute- and sense-based meaning is important for at least two reasons. Firstly, many practical NLP applications require, for a given word in context, to explicitly identify its sense from a sense inventory such as WordNet, as in the word sense disambiguation [13] task, or to group together contexts pertaining to the same sense, as in the word senseinduction (WSI) [14] task. Secondly, even when detecting senses is not an end goal in itself, it is important to have a way of validating substitution-based representations, which can be achieved by comparing it to the more established sense-based representation.