Information Retrieval Using Effective Bigram Topic Modeling

DOI

10.2991/978-94-6463-136-4_68How to use a DOI?

Keywords

Natural language Processing; topic modeling; bigram

Abstract

Many fields, such as film reviews, recommendation systems, and language processing, have effectively adopted and utilized topic modeling with Latent Dirichlet Allocation (LDA). Many texts analysis tasks, however, rely heavily on sentence construction and words to capture the meaning of text. However, word coexistence plays an important role in retrieving significant data. In this paper we present a novel method which discovers topics and topical phrases using language modeling. Proposed bigram extended LDA gives promising results to discover latent research areas in research articles and efficient classification of research articles. Experimental results are carried out to test the efficiency of proposed method.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Vrishali A. Chakkarwar
AU  - Sharvari C. Tamane
PY  - 2023
DA  - 2023/05/01
TI  - Information Retrieval Using Effective Bigram Topic Modeling
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 784
EP  - 791
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_68
DO  - 10.2991/978-94-6463-136-4_68
ID  - Chakkarwar2023
ER  -