TY  - JOUR
UR  - 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
PB  - Springer Science and Business Media Deutschland GmbH
N1  - 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
SP  - 1033
TI  - State-of-the Art: Short Text Semantic Similarity (STSS) Techniques in Question Answering Systems (QAS)
SN  - 18761100
ID  - scholars17408
A1  - Amur, Z.H.
A1  - Hooi, Y.
A1  - Sodhar, I.N.
A1  - Bhanbhro, H.
A1  - Dahri, K.
Y1  - 2022///
AV  - none
N2  - 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.
VL  - 758
JF  - Lecture Notes in Electrical Engineering
KW  - Artificial intelligence; Information retrieval; Natural language processing systems; Search engines
KW  -  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
KW  -  Semantics
EP  - 1044
ER  -