International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 5 September-October 2024 Submit your research before last 3 days of October to publish your research paper in the issue of September-October.

Sentence Modelling with Convolutional Neural Networks for Enhancing Natural Language Understanding: A Comprehensive Exploration

Author(s) Harsh Kumar Saha, Priyanka Dubey, Vibhor Srivastava
Country India
Abstract The paper presents a novel approach to implementing Convolutional Neural Networks (CNN) for Semantic Sentence Modelling, a significant advancement in Natural Language Processing. This model categorizes sentiments in text as Positive, Negative, or Neutral, improving the accuracy and efficiency of Sentiment Analysis Systems. Its applications include Opinion Mining, Paraphrase Detection, and Discourse Analysis. The paper emphasizes the need for sophisticated tools to understand and analyze sentiments in various languages, highlighting the potential of dedicated CNN models.
Keywords Convolutional Neural Network (CNN), Natural Language Processing, Lexicons, Deep Belief Network (DBN), BERT (Bidirectional Encoder Representation from Transformers), Lemmatization, Hyperparameter, Word Sense Disambiguation (WSD), Corpus, Tokenization.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-30
Cite This Sentence Modelling with Convolutional Neural Networks for Enhancing Natural Language Understanding: A Comprehensive Exploration - Harsh Kumar Saha, Priyanka Dubey, Vibhor Srivastava - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.18964
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.18964
Short DOI https://doi.org/gts4r3

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