<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The rapid growth of natural language processing (NLP) applications, such as text summarization, speech recognition, information extraction, and machine translation, has led to the development of structured query language (SQL) for extracting information from structured data. However, due to limited resources, converting Natural Language (NL) queries to SQL in Bengali is challenging. This article proposes an unsupervised machine learning model to find semantically Bengali closed words that can generate SQL from NL queries in Bengali. The main objective of the proposed system is to provide support in the creation of patient-oriented explanations and educational resources by simplifying intricate medical terminology. The major findings of the proposed system are as follows: The use of machine translation in the field of medicine facilitates the dissemination of healthcare information to a diverse international audience and improves the performance of entity recognition tasks, including the identification of medical conditions, drugs, or procedures within clinical notes or electronic health data. This system allows a naive user to extract health-related information from a healthcare-structured database without any knowledge of SQL. The system accepts a query and generates a response according to the query in Bengali language. Query tokenization and stop word removal are carried out in the preprocessing stage, and unsupervised machine learning techniques are implemented to process the input query sentence. Tokenized words are converted into vectors using the skip-gram model, with noise-contrastive estimation (NCE) applied to discriminate between actual and irrelevant words. Stochastic gradient descent (SGD) optimizes the model by randomly choosing a small amount of data from the dataset and using cosine similarity to measure closer words. The semantically closer words are found using an unsupervised learning method to generate the SQL.</p>
</abstract>

The rapid growth of natural language processing (NLP) applications, such as text summarization, speech recognition, information extraction, and machine translation, has led to the development of structured query language (SQL) for extracting information from structured data. However, due to limited resources, converting Natural Language (NL) queries to SQL in Bengali is challenging. This article proposes an unsupervised machine learning model to find semantically Bengali closed words that can generate SQL from NL queries in Bengali. The main objective of the proposed system is to provide support in the creation of patient-oriented explanations and educational resources by simplifying intricate medical terminology. The major findings of the proposed system are as follows: The use of machine translation in the field of medicine facilitates the dissemination of healthcare information to a diverse international audience and improves the performance of entity recognition tasks, including the identification of medical conditions, drugs, or procedures within clinical notes or electronic health data. This system allows a naive user to extract health-related information from a healthcare-structured database without any knowledge of SQL. The system accepts a query and generates a response according to the query in Bengali language. Query tokenization and stop word removal are carried out in the preprocessing stage, and unsupervised machine learning techniques are implemented to process the input query sentence. Tokenized words are converted into vectors using the skip-gram model, with noise-contrastive estimation (NCE) applied to discriminate between actual and irrelevant words. Stochastic gradient descent (SGD) optimizes the model by randomly choosing a small amount of data from the dataset and using cosine similarity to measure closer words. The semantically closer words are found using an unsupervised learning method to generate the SQL.

Medical nearest-word embedding technique implemented using an unsupervised machine learning approach for Bengali language

Computer Science and Engineering Department

School of Computing Science and Engineering

Symbiosis Institute of Technology, Symbiosis International (Deemed University)

School of Computer Science Engineering and Technology (SCSET)

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The rapid growth of natural language processing (NLP) applications, such as text summarization, speech recognition, information extraction, and machine...

Medical nearest-word embedding technique implemented using an unsupervised machine learning approach for Bengali language

Article Category: Article

Publié en ligne: 12 juin 2024

Pages: -

Reçu: 13 mai 2023

DOI: https://doi.org/10.2478/ijssis-2024-0018

Mots clés
natural language processing, skip-gram model, noise-contrastive estimation, stochastic gradient descent, structured query language

© 2024 Kailash Pati Mandal et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Medical nearest-word embedding technique implemented using an unsupervised machine learning approach for Bengali language

Article Category: Article

Publié en ligne: 12 juin 2024

Pages: -

Reçu: 13 mai 2023

DOI: https://doi.org/10.2478/ijssis-2024-0018

Mots clésnatural language processing, skip-gram model, noise-contrastive estimation, stochastic gradient descent, structured query language

© 2024 Kailash Pati Mandal et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
natural language processing, skip-gram model, noise-contrastive estimation, stochastic gradient descent, structured query language