AI-Driven Advanced Text Matching for Improved Information Capture and Retrieval

This paper presents an AI-pushed advanced text matching technique to improve facts capture and retrieval. The proposed approach makes use of deep getting to know and natural language processing algorithms to successfully compare texts and discover similarities and dissimilarities among them. The dee...

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Bibliographic Details
Published in2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) pp. 1 - 7
Main Authors Agrawal, Trapty, Kulhar, Kuldeep Singh, Singh, K. Ranjith, Indira, G., Shelke, Priya M., Kannagi, A
Format Conference Proceeding
LanguageEnglish
Published IEEE 29.12.2023
Subjects
dissimilarities
Natural language processing
presents
Semantics
similarities
statistics
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Summary:This paper presents an AI-pushed advanced text matching technique to improve facts capture and retrieval. The proposed approach makes use of deep getting to know and natural language processing algorithms to successfully compare texts and discover similarities and dissimilarities among them. The deep gaining knowledge of algorithm is trained on a textual content corpus that serves as enter statistics for the model. This corpus may be made from diverse styles of textual information depending at the goal of the assessment. To make certain accuracy, the model is outfitted with parameters consisting of word embedding, semantic similarity, and fuzzy string matching. Once skilled, the version is tested to discover false positives and fake negatives. Finally, the model is carried out to a hard and fast of statistics to quantify fits among texts and retrieve useful statistics. Consequences show a discount in false positives and a higher precision of the version in retrieving applicable statistics from given facts.
DOI:10.1109/SMARTGENCON60755.2023.10442849