Can modern statistical parsers lead to better natural language understanding for education?

Authors:
Umair Z. Ahmed;Arpit Kumar;Monojit Choudhury;Kalika Bali
Affiliations:
Microsoft Research India, Bangalore, India;Microsoft Research India, Bangalore, India;Microsoft Research India, Bangalore, India;Microsoft Research India, Bangalore, India
Venue:
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Year:
2012

Citing 8
Cited 0

A maximum-entropy-inspired parser

NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Three generative, lexicalised models for statistical parsing

ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Accurate unlexicalized parsing

ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Intricacies of Collins' Parsing Model

Computational Linguistics
Evaluating state-of-the-art treebank-style parsers for Coh-metrix and other learning technology environments

EdAppsNLP 05 Proceedings of the second workshop on Building Educational Applications Using NLP
A review of methods for automatic understanding of natural language mathematical problems

Artificial Intelligence Review
Statistical parsing with a context-free grammar and word statistics

AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Synthesizing geometry constructions

Proceedings of the 32nd ACM SIGPLAN conference on Programming language design and implementation

Quantified Score

Hi-index	0.00

Visualization

Abstract

We use state-of-the-art parsing technology to build GeoSynth --- a system that can automatically solve word problems in geometric constructions. Through our experiments we show that even though off-the-shelf parsers perform poorly on texts containing specialized vocabulary and long sentences, appropriate preprocessing of text before applying the parser and use of extensive domain knowledge while interpreting the parse tree can together help us circumvent parser errors and build robust domain specific natural language understanding modules useful for various educational applications.