Role of Computational Intelligence in Natural Language Processing

Das, Bishwa Ranjan; Mishra, Brojo Kishore

doi:10.1201/9780367808495-10

Cited by 4 publications

(10 citation statements)

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“…In order to effectually adjust the hyperparameters such as batch size, learning rate, and epoch count, the HSGO algorithm has been employed to it. The HSGO algorithm is a recently developed metaheuristic methodology that simulates Henry's law [ 3 ]. Like the population‐based method, HGSO started by means of setting the early values for a group of N solutions (S) or gases as well as based on the searching area the equation can be expressed as follows:

whereas Lb and Ub represent the lower and upper values in the searching space, correspondingly.…”

Section: Methodsmentioning

confidence: 99%

“…The intended audience consists of intelligent individuals. Research in the social sciences, linguistics, and cognitive sciences is necessary if artificial intelligence is to develop a system that can process natural language [ 3 ]. With the development of breakthroughs in artificial intelligence and natural language processing, humans and computers can now converse in natural language.…”

Section: Introductionmentioning

confidence: 99%

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The Performance of Artificial Intelligence Translation App in Japanese Language Education Guided by Deep Learning

Wang¹

2022

Computational Intelligence and Neuroscience

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With recent technological advances in wireless networks and the Internet, social media has become a vital part of the daily lives of people. Social media like Twitter, Facebook, and Instagram have enabled people to instantly share their thoughts and ideas about a particular topic or person’s life. Emotion classification in Twitter data remains a hot search topic in the field of artificial intelligence (AI). Though several models have been developed for tweet data in English, it is still needed to develop an effective tweet emotion classification for the Japanese language. In this aspect, this work develops a new artificial intelligence with an Optimal Long Short-Term Memory-Based Japanese Tweet Emotion Classification (OLSTM-JTCC) model in wireless networks. The proposed OLSTM-JTCC technique aims to examine emotions and categorises them into proper class labels. The proposed OLSTM-JTCC technique initially employs the TF-IFD model for the extraction of feature vectors. Besides, the OLSTM model is used to classify the tweet data into different types of emotions that exist within it. In order to improve the classification capability of the LSTM model, the Henry gas solubility optimization (HSGO) algorithm is applied as a hyperparameter optimizer. The performance validation of the OLSTM-JTCC technique took place using Japanese tweets, and the comparative results highlighted the better performance of the OLSTM-JTCC technique in terms of different measures.

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whereas Lb and Ub represent the lower and upper values in the searching space, correspondingly.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Performance of Artificial Intelligence Translation App in Japanese Language Education Guided by Deep Learning

Wang¹

2022

Computational Intelligence and Neuroscience

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“…It is easy to give the context of the literature to people who belong to different domains. Thus, it is important to have a computational, analytical, and sentimental analysis of the text to get meaningful insights [14] In [15], the authors have derived interesting insights from the English translation of Mahabharata [2] by applying Pre-processing, POS tagging, Co-occurrence analysis, sentiment analysis of text and characters, and emotional analysis. The Insights which are given about the character and phenomena are versatile enough to use in different domains.…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to [16] paper, The important characters of the epic Arjuna and Bheema had a common struggle and they trust each other abilities more intensely, this is also derived by [15] in the sentimental analysis across the text that "Arjuna and Bheema faced more negativity around them". In paper [16], the author brings the concept of considering the human values while designing the AI Agents.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating Related Words Computationally Using Language Model from the Mahabharata an Indian Epic

Gadesha¹,

Joshi²,

Naik³

2022

Lecture Notes in Networks and Systems

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Mahabharata' is the most popular among many Indian pieces of literature referred to in many domains for completely different purposes. This text itself is having various dimension and aspects which is useful for the human being in their personal life and professional life. This Indian Epic is originally written in the Sanskrit Language. Now in the era of Natural Language Processing, Artificial Intelligence, Machine Learning, and Human-Computer interaction this text can be processed according to the domain requirement. It is interesting to process this text and get useful insights from Mahabharata. The limitation of the humans while analyzing Mahabharata is that they always have a sentiment aspect towards the story narrated by the author. Apart from that, the human cannot memorize statistical or computational details, like which two words are frequently coming in one sentence? What is the average length of the sentences across the whole literature? Which word is the most popular word across the text, what are the lemmas of the words used across the sentences? Thus, in this paper, we propose an NLP pipeline to get some statistical and computational insights along with the most relevant word searching method from the largest epic 'Mahabharata'. We stacked the different text-processing approaches to articulate the best results which can be further used in the various domain where Mahabharata needs to be referred.

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“…Surprisingly, research on the Indian literary tradition has been very sparse, even for prominent works such as the Mahabharata (Figure 1). In A Computational Analysis of the Mahabharata, Das et al [8] provide one of the more visible applications of NLP techniques and analysis to the epic. In contrast to the LSA procedure employed in this study, Das et al opted for the much simpler and straightforward procedure of co-occurrence analysis to generate their social network.…”

Section: Introductionmentioning

confidence: 99%

A Quantitative Social Network Analysis of the Character Relationships in the Mahabharata

Gultepe,

Mathangi

2023

Heritage

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Despite the advances in computational literary analysis of Western literature, in-depth analysis of the South Asian literature has been lacking. Thus, social network analysis of the main characters in the Indian epic Mahabharata was performed, in which it was prepossessed into verses, followed by a term frequency–inverse document frequency (TF-IDF) transformation. Then, Latent Semantic Analysis (LSA) word vectors were obtained by applying compact Singular Value Decomposition (SVD) on the term–document matrix. As a novel innovation to this study, these word vectors were adaptively converted into a fully connected similarity matrix and transformed, using a novel locally weighted K-Nearest Neighbors (KNN) algorithm, into a social network. The viability of the social networks was assessed by their ability to (i) recover individual character-to-character relationships; (ii) embed the overall network structure (verified with centrality measures and correlations); and (iii) detect communities of the Pandavas (protagonist) and Kauravas (antagonist) using spectral clustering. Thus, the proposed scheme successfully (i) predicted the character-to-character connections of the most important and second most important characters at an F-score of 0.812 and 0.785, respectively, (ii) recovered the overall structure of the ground-truth networks by matching the original centralities (corr. > 0.5, p < 0.05), and (iii) differentiated the Pandavas from the Kauravas with an F-score of 0.749.

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Role of Computational Intelligence in Natural Language Processing

Cited by 4 publications

References 0 publications

The Performance of Artificial Intelligence Translation App in Japanese Language Education Guided by Deep Learning

The Performance of Artificial Intelligence Translation App in Japanese Language Education Guided by Deep Learning

Estimating Related Words Computationally Using Language Model from the Mahabharata an Indian Epic

A Quantitative Social Network Analysis of the Character Relationships in the Mahabharata

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