JU NITM at IJCNLP-2017 Task 5: A Classification Approach for Answer Selection in Multi-choice Question Answering System

Sandip Sarkar, Dipankar Das, Partha Pakray


Abstract
This paper describes the participation of the JU NITM team in IJCNLP-2017 Task 5: “Multi-choice Question Answering in Examinations”. The main aim of this shared task is to choose the correct option for each multi-choice question. Our proposed model includes vector representations as feature and machine learning for classification. At first we represent question and answer in vector space and after that find the cosine similarity between those two vectors. Finally we apply classification approach to find the correct answer. Our system was only developed for the English language, and it obtained an accuracy of 40.07% for test dataset and 40.06% for valid dataset.
Anthology ID:
I17-4036
Volume:
Proceedings of the IJCNLP 2017, Shared Tasks
Month:
December
Year:
2017
Address:
Taipei, Taiwan
Editors:
Chao-Hong Liu, Preslav Nakov, Nianwen Xue
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
213–216
Language:
URL:
https://aclanthology.org/I17-4036
DOI:
Bibkey:
Cite (ACL):
Sandip Sarkar, Dipankar Das, and Partha Pakray. 2017. JU NITM at IJCNLP-2017 Task 5: A Classification Approach for Answer Selection in Multi-choice Question Answering System. In Proceedings of the IJCNLP 2017, Shared Tasks, pages 213–216, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Cite (Informal):
JU NITM at IJCNLP-2017 Task 5: A Classification Approach for Answer Selection in Multi-choice Question Answering System (Sarkar et al., IJCNLP 2017)
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PDF:
https://preview.aclanthology.org/nschneid-patch-5/I17-4036.pdf