Open AccessDiscussion on The Data Extraction Strategy Of FAQ System Based On Chat Records
Author(s) -
Jia Li,
Xin Chen
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1871/1/012109
Subject(s) - computer science , classifier (uml) , ask price , precision and recall , grid , data mining , data extraction , machine learning , crossover , artificial intelligence , information extraction , recall , training set , service (business) , test data , knowledge extraction , information retrieval , linguistics , philosophy , geometry , economy , mathematics , medline , political science , law , economics , programming language
Q&A data based on chat records have one characteristic: customers ask, service answers. There is a large amount of knowledge between the questions and answers of customer service. By optimizing the Q&A extraction algorithm to extract knowledge, a very excellent Q&A library can be constructed, thus the accuracy of FAQ system is greatly improved. By analyzing the existing data, this paper cnosiders the extraction strategy of question answering from machine learning and non-machine learning respectively. Then We compares their performance from three aspects of precision, recall and Fl-score according to their different characteristics. In order to ensure the best classification performance, grid search and K-fold crossover are also used to test the optimized classifier performance. After selecting the optimal data extraction strategy, We developed a FAQ system use this strategy, the system results show that the performance is reliable.