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Classifier combination approach for question classification for Bengali question answering system.
- Source :
-
Sādhanā: Academy Proceedings in Engineering Sciences . Dec2019, Vol. 44 Issue 12, p1-14. 14p. - Publication Year :
- 2019
-
Abstract
- Question classification (QC) is a prime constituent of an automated question answering system. The work presented here demonstrates that a combination of multiple models achieves better classification performance than those obtained with existing individual models for the QC task in Bengali. We have exploited state-of-the-art multiple model combination techniques, i.e., ensemble, stacking and voting, to increase QC accuracy. Lexical, syntactic and semantic features of Bengali questions are used for four well-known classifiers, namely Naïve Bayes, kernel Naïve Bayes, Rule Induction and Decision Tree, which serve as our base learners. Single-layer question-class taxonomy with 8 coarse-grained classes is extended to two-layer taxonomy by adding 69 fine-grained classes. We carried out the experiments both on single-layer and two-layer taxonomies. Experimental results confirmed that classifier combination approaches outperform single-classifier classification approaches by 4.02% for coarse-grained question classes. Overall, the stacking approach produces the best results for fine-grained classification and achieves 87.79% of accuracy. The approach presented here could be used in other Indo-Aryan or Indic languages to develop a question answering system. [ABSTRACT FROM AUTHOR]
- Subjects :
- *QUESTION answering systems
*BENGALI language
*CLASSIFICATION
*DECISION trees
Subjects
Details
- Language :
- English
- ISSN :
- 02562499
- Volume :
- 44
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- Sādhanā: Academy Proceedings in Engineering Sciences
- Publication Type :
- Academic Journal
- Accession number :
- 140970501
- Full Text :
- https://doi.org/10.1007/s12046-019-1224-8