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:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/I17-4036.pdf