A comparison of greedy and optimal assessment of natural language student input using word-to-word similarity metrics

Authors:
Vasile Rus;Mihai Lintean
Affiliations:
The University of Memphis, Memphis, TN;The University of Memphis, Memphis, TN
Venue:
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Year:
2012

Citing 5
Cited 1

Unsupervised construction of large paraphrase corpora: exploiting massively parallel news sources

COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Deeper natural language processing for evaluating student answers in intelligent tutoring systems

AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
WordNet::Similarity: measuring the relatedness of concepts

HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Measuring the semantic similarity of texts

EMSEE '05 Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment
The PASCAL recognising textual entailment challenge

MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment

Experiments with semantic similarity measures based on LDA and LSA

SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present in this paper a novel, optimal semantic similarity approach based on word-to-word similarity metrics to solve the important task of assessing natural language student input in dialogue-based intelligent tutoring systems. The optimal matching is guaranteed using the sailor assignment problem, also known as the job assignment problem, a well-known combinatorial optimization problem. We compare the optimal matching method with a greedy method as well as with a baseline method on data sets from two intelligent tutoring systems, AutoTutor and iSTART.