Systematic Knowledge Acquisition for Question Analysis
D. Nguyen, D. Nguyen, and S. Pham. Proceedings of the International Conference Recent Advances in Natural Language Processing 2011, page 406--412. RANLP 2011 Organising Committee, (2011)
Abstract
For the task of turning a natural language question into an explicit intermediate representation of the complexity in question answering systems, all published works so far use rule-based approach to the best of our knowledge. We believe it is because of the complexity of the representation and the variety of question types and also there are no publicly available corpus of a decent size. In these rule-based approaches, the process of creating rules is not discussed. It is clear that manually creating the rules in an ad-hoc manner is very expensive and error-prone. In this paper, we focus on the process of creating those rules manually, in a way that consistency between rules is maintained and the effort to create a new rule is independent of the size of the current rule set. Experimental results are promising where our system achieves better performance and requires much less time and cognitive load compared to previous work.
%0 Conference Paper
%1 NguyenNP11
%A Nguyen, Dat Quoc
%A Nguyen, Dai Quoc
%A Pham, Son Bao
%B Proceedings of the International Conference Recent Advances in Natural Language Processing 2011
%D 2011
%I RANLP 2011 Organising Committee
%K information-extraction knowledge-acquisition myown question-analysis question-answering ripple-down-rules scrdr
%P 406--412
%T Systematic Knowledge Acquisition for Question Analysis
%U http://www.aclweb.org/anthology/R11-1056
%X For the task of turning a natural language question into an explicit intermediate representation of the complexity in question answering systems, all published works so far use rule-based approach to the best of our knowledge. We believe it is because of the complexity of the representation and the variety of question types and also there are no publicly available corpus of a decent size. In these rule-based approaches, the process of creating rules is not discussed. It is clear that manually creating the rules in an ad-hoc manner is very expensive and error-prone. In this paper, we focus on the process of creating those rules manually, in a way that consistency between rules is maintained and the effort to create a new rule is independent of the size of the current rule set. Experimental results are promising where our system achieves better performance and requires much less time and cognitive load compared to previous work.
@inproceedings{NguyenNP11,
abstract = {For the task of turning a natural language question into an explicit intermediate representation of the complexity in question answering systems, all published works so far use rule-based approach to the best of our knowledge. We believe it is because of the complexity of the representation and the variety of question types and also there are no publicly available corpus of a decent size. In these rule-based approaches, the process of creating rules is not discussed. It is clear that manually creating the rules in an ad-hoc manner is very expensive and error-prone. In this paper, we focus on the process of creating those rules manually, in a way that consistency between rules is maintained and the effort to create a new rule is independent of the size of the current rule set. Experimental results are promising where our system achieves better performance and requires much less time and cognitive load compared to previous work.},
added-at = {2012-03-31T01:34:10.000+0200},
author = {Nguyen, Dat Quoc and Nguyen, Dai Quoc and Pham, Son Bao},
biburl = {https://www.bibsonomy.org/bibtex/29fc0401a01973f915c80b778c82c7a5d/dqnguyen},
booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing 2011},
interhash = {4d5375a0aeec7362c7d8ff413c88276c},
intrahash = {9fc0401a01973f915c80b778c82c7a5d},
keywords = {information-extraction knowledge-acquisition myown question-analysis question-answering ripple-down-rules scrdr},
pages = {406--412},
publisher = {RANLP 2011 Organising Committee},
series = {RANLP 2011},
timestamp = {2014-04-16T03:49:18.000+0200},
title = {Systematic Knowledge Acquisition for Question Analysis},
url = {http://www.aclweb.org/anthology/R11-1056},
year = 2011
}