Short-text semantic similarity (STSS): Techniques, challenges and future perspectives

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F23%3A63570518" target="_blank" >RIV/70883521:28140/23:63570518 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2076-3417/13/6/3911" target="_blank" >https://www.mdpi.com/2076-3417/13/6/3911</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app13063911" target="_blank" >10.3390/app13063911</a>

Result language

angličtina

Original language name

Short-text semantic similarity (STSS): Techniques, challenges and future perspectives

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

APPLIED SCIENCES-BASEL

ISSN

2076-3417

e-ISSN

2076-3417

Volume of the periodical

13

Issue of the periodical within the volume

6

Country of publishing house

CH - SWITZERLAND

Number of pages

30

Pages from-to

1-30

UT code for WoS article

000954097200001

EID of the result in the Scopus database

2-s2.0-85152052419