Learning strategies for open-domain natural language question answering

Authors:
Eugene Grois
Affiliations:
University of Illinois, Urbana-Champaign, Urbana, Illinois
Venue:
ACLstudent '05 Proceedings of the ACL Student Research Workshop
Year:
2005

Citing 10
Cited 0

Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging

Computational Linguistics
Learning to Take Actions

Machine Learning
Natural language question answering: the view from here

Natural Language Engineering
A classification approach to word prediction

NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Deep Read: a reading comprehension system

ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Reading comprehension programs in a statistical-language-processing class

ANLP/NAACL-ReadingComp '00 Proceedings of the 2000 ANLP/NAACL Workshop on Reading comprehension tests as evaluation for computer-based language understanding sytems - Volume 6
A rule-based question answering system for reading comprehension tests

ANLP/NAACL-ReadingComp '00 Proceedings of the 2000 ANLP/NAACL Workshop on Reading comprehension tests as evaluation for computer-based language understanding sytems - Volume 6
Reinforcement learning: a survey

Journal of Artificial Intelligence Research
A formal framework for speedup learning from problems and solutions

Journal of Artificial Intelligence Research
Relational learning for NLP using linear threshold elements

IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2

Quantified Score

Hi-index	0.00

Visualization

Abstract

This work presents a model for learning inference procedures for story comprehension through inductive generalization and reinforcement learning, based on classified examples. The learned inference procedures (or strategies) are represented as of sequences of transformation rules. The approach is compared to three prior systems, and experimental results are presented demonstrating the efficacy of the model.