eprintid: 18728
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/87/28
datestamp: 2024-06-04 14:11:07
lastmod: 2024-06-04 14:11:07
status_changed: 2024-06-04 14:03:57
type: article
metadata_visibility: show
creators_name: Amur, Z.H.
creators_name: Kwang Hooi, Y.
creators_name: Bhanbhro, H.
creators_name: Dahri, K.
creators_name: Soomro, G.M.
title: Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
ispublished: pub
note: cited By 9
abstract: In natural language processing, short-text semantic similarity (STSS) is a very prominent field. It has a significant impact on a broad range of applications, such as question�answering systems, information retrieval, entity recognition, text analytics, sentiment classification, and so on. Despite their widespread use, many traditional machine learning techniques are incapable of identifying the semantics of short text. Traditional methods are based on ontologies, knowledge graphs, and corpus-based methods. The performance of these methods is influenced by the manually defined rules. Applying such measures is still difficult, since it poses various semantic challenges. In the existing literature, the most recent advances in short-text semantic similarity (STSS) research are not included. This study presents the systematic literature review (SLR) with the aim to (i) explain short sentence barriers in semantic similarity, (ii) identify the most appropriate standard deep learning techniques for the semantics of a short text, (iii) classify the language models that produce high-level contextual semantic information, (iv) determine appropriate datasets that are only intended for short text, and (v) highlight research challenges and proposed future improvements. To the best of our knowledge, we have provided an in-depth, comprehensive, and systematic review of short text semantic similarity trends, which will assist the researchers to reuse and enhance the semantic information. © 2023 by the authors.
date: 2023
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152052419&doi=10.3390%2fapp13063911&partnerID=40&md5=4eef64a1fb0ff17bb4952c04c42fa64f
id_number: 10.3390/app13063911
full_text_status: none
publication: Applied Sciences (Switzerland)
volume: 13
number: 6
refereed: TRUE
citation:   Amur, Z.H. and Kwang Hooi, Y. and Bhanbhro, H. and Dahri, K. and Soomro, G.M.  (2023) Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives.  Applied Sciences (Switzerland), 13 (6).