A Survey of Text Representation Methods and Their Genealogy

Siebers P, Janiesch C, Zschech P (2022)

Publication Type: Journal article

Publication year: 2022

Journal

IEEE Access Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 10

Pages Range: 96492-96513

DOI: 10.1109/ACCESS.2022.3205719

Abstract

In recent years, with the advent of highly scalable artificial-neural-network-based text representation methods the field of natural language processing has seen unprecedented growth and sophistication. It has become possible to distill complex linguistic information of text into multidimensional dense numeric vectors with the use of the distributional hypothesis. As a consequence, text representation methods have been evolving at such a quick pace that the research community is struggling to retain knowledge of the methods and their interrelations. We contribute threefold to this lack of compilation, composition, and systematization by providing a survey of current approaches, by arranging them in a genealogy, and by conceptualizing a taxonomy of text representation methods to examine and explain the state-of-the-art. Our research is a valuable guide and reference for artificial intelligence researchers and practitioners interested in natural language processing applications such as recommender systems, chatbots, and sentiment analysis.

Authors with CRIS profile

Patrick Zschech Assistant Professorship for Intelligent Information Systems

Involved external institutions

Technische Universität Dortmund DE Germany (DE) Technische Universität Dresden DE Germany (DE)

How to cite

APA:

Siebers, P., Janiesch, C., & Zschech, P. (2022). A Survey of Text Representation Methods and Their Genealogy. IEEE Access, 10, 96492-96513. https://doi.org/10.1109/ACCESS.2022.3205719

MLA:

Siebers, Philipp, Christian Janiesch, and Patrick Zschech. "A Survey of Text Representation Methods and Their Genealogy." IEEE Access 10 (2022): 96492-96513.

BibTeX: Download