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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