Analytical Review on Word Sense Disambiguation
By: Parekh, Manali
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Contributor(s): Balani, Prem.
Publisher: New Delhi STM Journals 2018Edition: Vol, 5 (1), Jan-Apr.Description: 32-37p.Subject(s): Computer EngineeringOnline resources: Click Here
In:
Journal of artificial intelligence research and advances (JoAIRA)Summary: Due to high Semantic Ambiguity that is associated with language, Word Sense Disambiguation is an open problem of Natural Language Processing and Ontology in Computer Linguistics. For instance, the word Head can mean the part of body and a person in charge of organization. The task of automatically assigning the most suitable meaning to a polysemous word can be defined as Word Sense Disambiguation. Several approaches have been done, from Dictionary-and Knowledge-based method that uses Lexical resource like WordNet, to Supervised-Machine learning method that use trained classifier on manually sense-annotated corpus, to cluster based Un-Supervised methods. These approaches have been applied for several languages like German, English, French, Chinese, and some Indian languages like Assamese, Hindi, Marathi and Malayalam. This survey aims to present about some aspect of word sense disambiguation and its approaches, focusing more on Unsupervised Graph based approach.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2021-2021458 |
Due to high Semantic Ambiguity that is associated with language, Word Sense Disambiguation is an open problem of Natural Language Processing and Ontology in Computer Linguistics. For instance, the word Head can mean the part of body and a person in charge of organization. The task of automatically assigning the most suitable meaning to a polysemous word can be defined as Word Sense Disambiguation. Several approaches have been done, from Dictionary-and Knowledge-based method that uses Lexical resource like WordNet, to Supervised-Machine learning method that use trained classifier on manually sense-annotated corpus, to cluster based Un-Supervised methods. These approaches have been applied for several languages like German, English, French, Chinese, and some Indian languages like Assamese, Hindi, Marathi and Malayalam. This survey aims to present about some aspect of word sense disambiguation and its approaches, focusing more on Unsupervised Graph based approach.
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