Mohasseb, Alaa, Bader, Mohamed, Cocea, Mihaela and Liu, Han
2018.
Improving imbalanced question classification using structured smote based approach.
Presented at: International Conference on Machine Learning and Cybernetics (ICMLC 2018),
Chengdu, China,
15-18 July 2018.
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Abstract
Questions Classification (QC) is one of the most popular text classification applications. QC plays an important role in question-answering systems. However, as in many real-world classification problems, QC may suffer from the problem of class imbalance. The classification of imbalanced data has been a key problem in machine learning and data mining. In this paper, we propose a framework that deals with the class imbalance using a hierarchical SMOTE algorithm for balancing different types of questions. The proposed framework is grammar-based, which involves using the grammatical pattern for each question and using machine learning algorithms to classify them. Experimental results imply that the proposed framework demonstrates a good level of accuracy in identifying different question types and handling class imbalance.
Item Type: |
Conference or Workshop Item
(Paper)
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Date Type: |
Completion |
Status: |
Unpublished |
Schools: |
Computer Science & Informatics |
Subjects: |
Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Related URLs: |
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Date of First Compliant Deposit: |
6 July 2018 |
Last Modified: |
15 Oct 2020 01:33 |
URI: |
http://orca.cf.ac.uk/id/eprint/112927 |
Available Versions of this Item
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Improving imbalanced question classification using structured smote based approach. (deposited 06 Jul 2018 10:04)
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