The Review of Natural Language Processing Applications with Emphasis on Machine Learning Implementations

Nagarhalli, Tatwadarshi P.; Mhatre, Sneha; Patil, Sanket; Patil, Prafulla D.

doi:10.1109/icears53579.2022.9752326

Cited by 11 publications

(2 citation statements)

References 14 publications

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“…Social networks like Facebook, LinkedIn (3) and others generate a huge amount of data on the world wide web but nowadays due to the user-friendly technologies users can create, share new opinions, ideas and content either through spoken words or typed text, resulting in massive data. This unstructured data cannot be processed by the machines directly and hence needs machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

HiTEK Pre-processing for Speech and Text: NLP

Rudrappa¹,

Reddy²,

Hanumanthappa³

2023

IJST

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Objective: To develop a system that accepts a sentence consisting of two and/or four languages and convert it to a target language text, termed as Cross Language Speech Identification and Text Translation System. Methods: A combinatorial model consisting of Hidden Markov Model, Artificial Neural Networks, Deep Neural Networks and Gaussian Mixture Model are utilized for direct and indirect speech mapping. Trained dataset consisting of thousand phonemes for each of the Hindi, Telugu, English and Kannada languages, initially for bank, hospital domains, later the grammatical phonemes of each language were added and wave files consisting of cross lingual spoken sentence were created which incurred a six months period to build from scratch, as cross lingual vocal data-set is not available. Hindi language dataset Shabdanjali was also referred. The basic parameters considered for creation of structured dataset are loudness, pause, pitch, tone, noise cancellation, sampling frequency, threshold etc. Findings: Comparative analysis of various techniques, target languages and features are tabulated. Research idea emerged from the comparative analysis of Monolingual Systems where there was a gap for cross lingual speech to text translation. The architecture can be enhanced in future for other regional languages of India. Novelty: A new bench mark for Cross Language dataset was created. This work presents CLSITT tool applicable in transforming public speeches spoken in multiple languages to a selected target language and the tool is helpful for a regional news editor, rural and agricultural activities, medical applications, defence and so on.

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Section: Introductionmentioning

confidence: 99%

HiTEK Pre-processing for Speech and Text: NLP

Rudrappa¹,

Reddy²,

Hanumanthappa³

2023

IJST

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“…Sentiment analysis is a natural language processing (NLP) technique that is commonly used to identify, extract, and quantify subjective information from text data [ 47 ]. Sentiment analysis is mainly used to analyze the text and determine the sentiment score.…”

Section: Introductionmentioning

confidence: 99%

Neuromorphic Sentiment Analysis Using Spiking Neural Networks

Chunduri,

Perera

2023

Sensors

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Over the past decade, the artificial neural networks domain has seen a considerable embracement of deep neural networks among many applications. However, deep neural networks are typically computationally complex and consume high power, hindering their applicability for resource-constrained applications, such as self-driving vehicles, drones, and robotics. Spiking neural networks, often employed to bridge the gap between machine learning and neuroscience fields, are considered a promising solution for resource-constrained applications. Since deploying spiking neural networks on traditional von-Newman architectures requires significant processing time and high power, typically, neuromorphic hardware is created to execute spiking neural networks. The objective of neuromorphic devices is to mimic the distinctive functionalities of the human brain in terms of energy efficiency, computational power, and robust learning. Furthermore, natural language processing, a machine learning technique, has been widely utilized to aid machines in comprehending human language. However, natural language processing techniques cannot also be deployed efficiently on traditional computing platforms. In this research work, we strive to enhance the natural language processing traits/abilities by harnessing and integrating the SNNs traits, as well as deploying the integrated solution on neuromorphic hardware, efficiently and effectively. To facilitate this endeavor, we propose a novel, unique, and efficient sentiment analysis model created using a large-scale SNN model on SpiNNaker neuromorphic hardware that responds to user inputs. SpiNNaker neuromorphic hardware typically can simulate large spiking neural networks in real time and consumes low power. We initially create an artificial neural networks model, and then train the model using an Internet Movie Database (IMDB) dataset. Next, the pre-trained artificial neural networks model is converted into our proposed spiking neural networks model, called a spiking sentiment analysis (SSA) model. Our SSA model using SpiNNaker, called SSA-SpiNNaker, is created in such a way to respond to user inputs with a positive or negative response. Our proposed SSA-SpiNNaker model achieves 100% accuracy and only consumes 3970 Joules of energy, while processing around 10,000 words and predicting a positive/negative review. Our experimental results and analysis demonstrate that by leveraging the parallel and distributed capabilities of SpiNNaker, our proposed SSA-SpiNNaker model achieves better performance compared to artificial neural networks models. Our investigation into existing works revealed that no similar models exist in the published literature, demonstrating the uniqueness of our proposed model. Our proposed work would offer a synergy between SNNs and NLP within the neuromorphic computing domain, in order to address many challenges in this domain, including computational complexity and power consumption. Our proposed model would not only enhance the capabilities of sentiment analysis but also contribute to the advancement of brain-inspired computing. Our proposed model could be utilized in other resource-constrained and low-power applications, such as robotics, autonomous, and smart systems.

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Application of Machine Learning and Deep Learning in High Performance Computing

Murugaiah

2024

Series in BioEngineering

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The Review of Natural Language Processing Applications with Emphasis on Machine Learning Implementations

Cited by 11 publications

References 14 publications

HiTEK Pre-processing for Speech and Text: NLP

HiTEK Pre-processing for Speech and Text: NLP

Neuromorphic Sentiment Analysis Using Spiking Neural Networks

Application of Machine Learning and Deep Learning in High Performance Computing

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