@inproceedings{jauhar2016tables,
author = {Jauhar, Sujay Kumar and Turney, Peter and Hovy, Eduard},
title = {Tables as Semi-structured Knowledge for Question Answering},
booktitle = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016)},
year = {2016},
month = {August},
abstract = {Question answering requires access to a knowledge base to check facts and reason about information. Knowledge in the form of natural language text is easy to acquire, but difficult for automated reasoning. Highly-structured knowledge bases can facilitate reasoning, but are difficult to acquire. In this paper we explore tables as a semi-structured formalism that provides a balanced compromise to this trade-off. We first use the structure of tables to guide the construction of a dataset of over 9000 multiple-choice questions with rich alignment annotations, easily and efficiently via crowd-sourcing. We then use this annotated data to train a semi-structured feature-driven model for question answering that uses tables as a knowledge base. In benchmark evaluations, we significantly outperform both a strong unstructured retrieval baseline and a highly structured Markov Logic Network model.},
url = {http://approjects.co.za/?big=en-us/research/publication/tables-semi-structured-knowledge-question-answering/},
pages = {474-483},
}