Abstract
In this paper, we propose an approach to search for the best semantic match of a user query for the question answering system. To achieve this, we make use of word embeddings with a help of trained model using the question answering corpus and its variations to detect the word senses of search queries by the user and show the top best matches which belongs to the same class of question answering pairs and retrieves the corresponding answer to the user. This solution is deployed in ticketing system in large IT industry to automate the user query to retrieve the answers. Word level to context level semantics are achieved through trained model of semantic knowledge with word embeddings.
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Trivedi, S., Agnihotram, G., Jagan, B., Naik, P. (2018). A Question Answering Model Based on Semantic Matcher for Support Ticketing System. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 906. Springer, Singapore. https://doi.org/10.1007/978-981-13-1813-9_17
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DOI: https://doi.org/10.1007/978-981-13-1813-9_17
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