relation: https://khub.utp.edu.my/scholars/17408/
title: State-of-the Art: Short Text Semantic Similarity (STSS) Techniques in Question Answering Systems (QAS)
creator: Amur, Z.H.
creator: Hooi, Y.
creator: Sodhar, I.N.
creator: Bhanbhro, H.
creator: Dahri, K.
description: Semantics can be used to assess responses in question answering systems (QAS). The responses are typically short sentences. Assessing short sentences for similarity with the expected answer is a challenge for Artificial Intelligence. Unlike long paragraphs, short texts lacks the adequate and accurate semantic information. Existing algorithms don�t work well for short texts due to insufficient semantic information. This Paper provides the state of art on semantic similarity techniques and proposed the research framework to enhance the accuracy of short texts. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2022
type: Article
type: PeerReviewed
identifier:   Amur, Z.H. and Hooi, Y. and Sodhar, I.N. and Bhanbhro, H. and Dahri, K.  (2022) State-of-the Art: Short Text Semantic Similarity (STSS) Techniques in Question Answering Systems (QAS).  Lecture Notes in Electrical Engineering, 758.  pp. 1033-1044.  ISSN 18761100     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142695086&doi=10.1007%2f978-981-16-2183-3_98&partnerID=40&md5=3a04c9e8b5f7f6d80bbafe8245a22c0c
relation: 10.1007/978-981-16-2183-3₉₈
identifier: 10.1007/978-981-16-2183-3₉₈