@article{scholars17408,
            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},
       publisher = {Springer Science and Business Media Deutschland GmbH},
         journal = {Lecture Notes in Electrical Engineering},
            year = {2022},
             doi = {10.1007/978-981-16-2183-3{$_9$}{$_8$}},
          volume = {758},
           title = {State-of-the Art: Short Text Semantic Similarity (STSS) Techniques in Question Answering Systems (QAS)},
           pages = {1033--1044},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142695086&doi=10.1007\%2f978-981-16-2183-3\%5f98&partnerID=40&md5=3a04c9e8b5f7f6d80bbafe8245a22c0c},
        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 don{\^a}??t work well for short texts due to insufficient semantic information. This Paper provides the state of art on semantic similarity{\^A} techniques and proposed the research framework to enhance the accuracy of short texts. {\^A}{\copyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},
          author = {Amur, Z. H. and Hooi, Y. and Sodhar, I. N. and Bhanbhro, H. and Dahri, K.},
        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},
            isbn = {9789811621826},
            issn = {18761100}
}