International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
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A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
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Volume 6 Issue 5
September-October 2024
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Sentence Modelling with Convolutional Neural Networks for Enhancing Natural Language Understanding: A Comprehensive Exploration
Author(s) | Harsh Kumar Saha, Priyanka Dubey, Vibhor Srivastava |
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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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E-ISSN 2582-2160
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