eprintid: 17408 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/01/74/08 datestamp: 2023-12-19 03:23:48 lastmod: 2023-12-19 03:23:48 status_changed: 2023-12-19 03:08:00 type: article metadata_visibility: show creators_name: Amur, Z.H. creators_name: Hooi, Y. creators_name: Sodhar, I.N. creators_name: Bhanbhro, H. creators_name: Dahri, K. title: State-of-the Art: Short Text Semantic Similarity (STSS) Techniques in Question Answering Systems (QAS) ispublished: pub keywords: Artificial intelligence; Information retrieval; Natural language processing systems; Search engines, Accuracy question answering system; Information contents; Question answering systems; Research frameworks; Semantic similarity; Semantics Information; Short texts; State of the art; Text assessment; Text Summarisation, Semantics note: cited By 3; Conference of 1st International Conference on Artificial Intelligence for Smart Community, AISC 2020 ; Conference Date: 17 December 2020 Through 18 December 2020; Conference Code:286319 abstract: 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 donâ��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. date: 2022 publisher: Springer Science and Business Media Deutschland GmbH official_url: 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 id_number: 10.1007/978-981-16-2183-3₉₈ full_text_status: none publication: Lecture Notes in Electrical Engineering volume: 758 pagerange: 1033-1044 refereed: TRUE isbn: 9789811621826 issn: 18761100 citation: 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