Unsupervised Context-Based Probabilistic Text Classification

Authors

  • Srivastava A Data Science Developer, Tata Consultancy Services Pvt. Ltd, Chennai, India
  • Gunasekar L Data Scientist, Tata Consultancy Services Pvt. Ltd, Chennai, India
  • Lakshmi V B Principal Scientist, Tata Consultancy Services Pvt. Ltd, Chennai, India
  • Devaraj N Domain Consultant, Tata Consultancy Services Pvt. Ltd, Chennai, India

DOI:

https://doi.org/10.26438/ijcse/v9i12.914

Keywords:

Document Categorization, , Keywords Extraction, Concept Learnin, Multi-class Probabilistic Classification, Content Mining

Abstract

Text classification is the one of the primary tasks in Natural Language Processing (NLP). Key phrase extraction is the fundamental component that aids the mapping of documents to a set of emblematic phrases. For example, a category that includes IT documents can be described as “Information and Computer” or “Information and Technology”. If a text document includes keywords such as “issue” and “order”, then it belongs to “Issue Category”. Multiple pre-trained and deep learning approaches are available now-a-days for semantic analysis. Word embeddings are predominant technique that provides light to find the semantic similarity between tokens/phrases using word vectors. The most widely used word embeddings are GloVe, Word2vec, BERT etc. Experimental results show that the strategy produced by this study have more precision and simplicity than that of other methods.

References

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Published

2021-12-31
CITATION
DOI: 10.26438/ijcse/v9i12.914
Published: 2021-12-31

How to Cite

[1]
A. Srivastava, L. Gunasekar, B. Lakshmi V, and N. Devaraj, “Unsupervised Context-Based Probabilistic Text Classification”, Int. J. Comp. Sci. Eng., vol. 9, no. 12, pp. 9–14, Dec. 2021.

Issue

Vol. 9 No. 12 (2021): IJCSE December Edition

Section

Research Article

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