An Efficient Context-dependent Lexical Information Detection using Word Embeddings and Deep Machine Learning Classifiers for Unstructured Textual Contents

Amit Shukla¹ , Rajendra Gupta²

Section:Research Paper, Product Type: Journal-Paper
Vol.11 , Issue.5 , pp.54-59, Oct-2023

Online published on Oct 31, 2023

Copyright © Amit Shukla, Rajendra Gupta . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The term "context dependent" refers to a type of word representation that enables machine learning algorithms to distinguish words that have similar meanings. It is a feature learning technique that uses probabilistic models, dimension reduction, or neural networks on the word co-occurrence vector matrix to map words into real-number vectors. We address the problem of recognizing unstructured context-dependent lexical information in unstructured data containers in the research study. We investigate a method that employs word embedding for automatic context and relevant feature detection, as well as a deep neural network for classification. Using publicly accessible tweet and image datasets, we present an alternative model that use Conventional Machine Learning (CML) classifiers and a rule-based model. The proposed method outperforms the alternatives of earlier research. The CLID is analysed in terms of four aspects on the basis of Context-Centred Extraction of Concepts (CCEC). The proposed word embeddings method CCEC gives benefit from a neural-network methods ability to encode textual information by converting meaningful text information into numeric values.

Key-Words / Index Term :
Context-dependent Lexical Information, Word Embeddings, Deep ML Classifier, Unstructured Textual Contents

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