HybQA: Hybrid Deep Relation Extraction for Question Answering on Freebase

Reham Mohamed, Nagwa M. El-Makky, Khaled Nagi

2017

Abstract

Question Answering over knowledge-based data is one of the most important Natural Language Processing tasks. Despite numerous efforts that have been made in this field, it is not yet in the mainstream. Question Answering can be formulated as a Relation Extraction task between the question focus entity and the expected answer. Therefore, it requires high accuracy to solve a dual problem where the relation and answer are unknown. In this work, we propose a HybQA, a Hybrid Relation Extraction system to provide high accuracy for the Relation Extraction and the Question Answering tasks over Freebase. We propose a hybrid model that combines different types of state-of-the-art deep networks that capture the relation type between the question and the expected answer from different perspectives and combine their outputs to provide accurate relations. We then use a joint model to infer the possible relation and answer pairs simultaneously. However, since Relation Extraction might still be prone to errors due to the large size of the knowledge-base corpus (Freebase), we finally use evidence from Wikipedia as an unstructured knowledge base to select the best relation-answer pair. We evaluate the system on WebQuestions data and show that the system achieves a statistical significant improvement over the existing state-of-the-art models and provides the best accuracy which is 57%.

Paper Citation

in Harvard Style

Mohamed R., M. El-Makky N. and Nagi K. (2017). HybQA: Hybrid Deep Relation Extraction for Question Answering on Freebase.In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, ISBN 978-989-758-272-1, pages 128-136. DOI: 10.5220/0006589301280136

in Bibtex Style

@conference{keod17,
author={Reham Mohamed and Nagwa M. El-Makky and Khaled Nagi},
title={HybQA: Hybrid Deep Relation Extraction for Question Answering on Freebase},
booktitle={Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD,},
year={2017},
pages={128-136},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006589301280136},
isbn={978-989-758-272-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD,
TI - HybQA: Hybrid Deep Relation Extraction for Question Answering on Freebase
SN - 978-989-758-272-1
AU - Mohamed R.
AU - M. El-Makky N.
AU - Nagi K.
PY - 2017
SP - 128
EP - 136
DO - 10.5220/0006589301280136