Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
A Fully functional query answering system has been quite popular among researchers. In this paper the design and implementation of an automatic query answering system is presented. In the proposed system, queries are given as input to the model and all related questions and possible answers related to that query above a threshold value is retrieved. In this approach the input query given is first preprocessed for normalization and standardization of data. Each questions and their answers are considered as individual documents with each questions linked with corresponding answer (answers are not preprocessed). Tokenization is performed on data and TF-IDF is used to generate representation vectors of documents. Input query is also converted to its vector form. In the proposed system, cosine similarity is used to find the similarity between the input query with the datas found in the dataset. This information can be used to give instantaneous reply to queries. The system tested and implemented successfully on kissan call centre data.
"A TF-IDF Method For Automatic Query Answering", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.4, Issue 1, page no.75 - 78, January-2019, Available :http://www.ijrti.org/papers/IJRTI1901012.pdf
Downloads:
000204739
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator