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Full-Text Articles in Social and Behavioral Sciences
Entropy Of Polysemantic Words For The Same Part Of Speech, Mihaela Colhon, Florentin Smarandache, Dan Valeriu Voinea
Entropy Of Polysemantic Words For The Same Part Of Speech, Mihaela Colhon, Florentin Smarandache, Dan Valeriu Voinea
Branch Mathematics and Statistics Faculty and Staff Publications
In this paper, a special type of polysemantic words, that is, words with multiple meanings for the same part of speech, are analyzed under the name of neutrosophic words. These words represent the most difficult cases for the disambiguation algorithms as they represent the most ambiguous natural language utterances. For approximate their meanings, we developed a semantic representation framework made by means of concepts from neutrosophic theory and entropy measure in which we incorporate sense related data. We show the advantages of the proposed framework in a sentiment classification task.
Word-Level Neutrosophic Sentiment Similarity, Florentin Smarandache, Mihaela Colhon, Stefan Vladutescu, Xenis Negrea
Word-Level Neutrosophic Sentiment Similarity, Florentin Smarandache, Mihaela Colhon, Stefan Vladutescu, Xenis Negrea
Branch Mathematics and Statistics Faculty and Staff Publications
In the specialised literature, there are many approaches developed for capturing textual measures: textual similarity, textual readability and textual sentiment. This paper proposes a new sentiment similarity measures between pairs of words using a fuzzy-based approach in which words are considered single-valued neutrosophic sets. We build our study with the aid of the lexical resource SentiWordNet 3.0 as our intended scope is to design a new word-level similarity measure calculated by means of the sentiment scores of the involved words. Our study pays attention to the polysemous words because these words are a real challenge for any application that processes …