AI-based NLP section discusses the application and effect of bag-of-words models and TF-IDF in NLP tasks

Shuying Dai (), Keqin Li (), Zhuolun Luo (), Peng Zhao (), Bo Hong (), Armando Zhu () and Jiabei Liu ()

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2024, vol. 5, issue 1, 13-21

Abstract: This paper delves into the practical applications and effectiveness of two prominent text representation methods, the Bag-of-Words (BoW) model and Term Frequency-Inverse Document Frequency (TF-IDF), in the realm of Natural Language Processing (NLP). It commences with an introductory overview of NLP and its pivotal role in the broader field of Artificial Intelligence (AI), elucidating the importance of enabling computers to comprehend and manipulate human language. Subsequently, a comprehensive elucidation of the underlying principles and implementation of these two methods is provided. By conducting a comparative analysis of their respective strengths and weaknesses, the paper endeavors to ascertain the most suitable model for a diverse range of scenarios. The study reveals that while the BoW model proves to be effective for tasks involving short text classification, TF-IDF emerges as the preferred choice for applications such as search engines and keyword extraction. This is attributed to TF-IDF's ability to discern the significance of words within a document in relation to a corpus, thereby mitigating the influence of common but less meaningful words. In conclusion, the paper highlights the significance of AI advancements in shaping the future landscape of NLP. The integration of neural networks and deep learning has revolutionized the field, enabling more sophisticated text representations and enhancing performance in areas such as speech recognition, machine translation, and sentiment analysis. The paper underscores the dynamic nature of NLP and its continual evolution in tandem with AI technologies, offering promising prospects for future research and application development.

Keywords: Natural Language Processing; Artificial Intelligence; Bag-of-Words (BoW); Term Frequency-Inverse Document Frequency (TF-IDF); Representation; Comparative Analysis; Short Text Classification; Search Engines (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://newjaigs.com/index.php/JAIGS/article/view/149 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:das:njaigs:v:5:y:2024:i:1:p:13-21:id:149

Access Statistics for this article

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 is currently edited by Justyna Żywiołek

More articles in Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 from Open Knowledge
Bibliographic data for series maintained by Open Knowledge ().