Automatic Classification of Community Question Answer (CQA) for Non Factoid Queries
| Autor: | R S Ramya, M Darshan, S. Sitharama Iyengar, N Sejal, K R Venugopal, Lalit M. Patnaik |
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| Rok vydání: | 2019 |
| Předmět: |
education.field_of_study
business.industry Computer science Factoid Population 02 engineering and technology computer.software_genre Support vector machine 030507 speech-language pathology & audiology 03 medical and health sciences ComputingMethodologies_PATTERNRECOGNITION 020204 information systems 0202 electrical engineering electronic engineering information engineering Task analysis The Internet Artificial intelligence Question answer 0305 other medical science education business computer Classifier (UML) Natural language processing Multinomial logistic regression |
| Zdroj: | 2019 IEEE 16th India Council International Conference (INDICON). |
| DOI: | 10.1109/indicon47234.2019.9028852 |
| Popis: | Owing to the steep increase in the Internet population, the content over the web is increasing exponentially so as Community Question Answer (CQA) have acquired very huge amount of questions and answers. In this article, a machine learning algorithms are utilized for Question Classification (QC) and Answer Classification (AC). We identify the category of the question posted and further map with the corresponding question. Similarly for the answers posted by the multiple user will be processed for category mapping. Here the result shows the effective classifier that can be chosen to perform the mapping task for both Question classifier as well as answer classifier. Here the results shows that, for Question Classification (QA), Linear Support Vector Classification (LSVC) is found to be best classifier and Multinomial Logistic Regression (MLR) is most suitable for Answer Classification (AC). Using the probability of overall possible outcomes of a particular answer will give a best answer. Experiments results shows that our method outperforms efficiently. |
| Databáze: | OpenAIRE |
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