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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Deep Learning: Unraveling Word Meanings with Language Models |
| Authors Name: | Dr. Amit Prakashrao Patil , Ms. Chhaya Suhas Patil , Mr.Vishal A. Pawar |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_197578
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| Published Paper Id: | IJNRD2305839 |
| Published In: | Volume 8 Issue 5, May-2023 |
| DOI: | |
| Abstract: | In Natural Language Processing (NLP), the term "word sense disambiguation" (WSD) pertains to the process of determining the specific meaning or sense of a word based on the context in which it is employed. The term polysemy refers to words that take on a different meaning depending on their context in a sentence. Tie, Bank, Interest, and Book are some examples of polysemy words. In Natural Language Processing, the task of WSD remains an open problem. WSD is easy for humans but challenging for automatic systems. Several WSD systems have been proposed, and it enables discrete word features to be extracted. These methods use a classifier that is trained using surrounding words and collocations in order to identify the words. By incorporating continuous words of surrounding words, this classifier can be improved. Recent improvements haven't been noticed by any of them. This is due to the fact that all systems use word representations that are independent of the context in which they are used. Recent research has demonstrated that contextualized word embeddings enhance several NLP tasks. BERT (Bidirectional Encoder Representations from Transformers) contains pre-trained contextualized word representations. BERT identifies the word that is most likely to be in a word that has been hidden in a sentence. In this paper we are giving introduction to transformers in NLP and the BERT (Bidirectional Encoder Representations from Transformers) model. We also explained here the limitations of transformer and work done using BERT model for WSD. |
| Keywords: | BERT, NLP, Deep Learning, Language Models |
| Cite Article: | "Deep Learning: Unraveling Word Meanings with Language Models", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.i301-i304, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305839.pdf |
| Downloads: | 000118751 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2305839 Registration ID: 197578 Published In: Volume 8 Issue 5, May-2023 DOI (Digital Object Identifier): Page No: i301-i304 Country: Shirpur, Maharashtra, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2305839 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2305839 |
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ISSN: 2456-4184
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