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Title: Ensemble classifier based approach for code-mixed cross-script question classification
Authors: Bhattacharjee, Debjyoti
Bhattacharya, Paheli
Keywords: Question Answering System
DRNTU::Engineering::Computer science and engineering
Mixed Script Information Retrieval
Issue Date: 2016
Source: Bhattacharjee, D., & Bhattacharya, P. (2016). Ensemble classifier based approach for code-mixed cross-script question classification. FIRE 2016 - Forum for Information Retrieval Evaluation, 1737, 119-121.
Abstract: With an increasing popularity of social-media, people post updates that aid other users in finding answers to their questions. Most of the user-generated data on social-media are in code-mixed or multi-script form, where the words are represented phonetically in a non-native script. We address the problem of Question-Classfication on social-media data. We propose an ensemble classifier based approach towards question classification when the questions are written in mixedscript, specifically, the Roman script for the Bengali language. We separately train Random Forests, One-Vs-Rest and k-NN classifiers and then build an ensemble classifier that combines the best from the three worlds. We achieve an accuracy of 82% approximately, suggesting that the method works well in the task.
Rights: © 2016 The Author(s). All rights reserved. This paper was published in CEUR Workshop Proceedings and is made available with permission of 2016 The Author(s).
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