View Full Document

Combining Bayesian Networks and Formal Reasoning for Semantic Classification of Student Utterances



View the full content.
View Full Document
View Full Document

21 views

Unformatted text preview:

Combining Bayesian Networks and Formal Reasoning for Semantic Classification of Student Utterances Maxim Makatchev a 1 and Kurt VanLehn b The Robotics Institute Carnegie Mellon University b Learning Research and Development Center University of Pittsburgh a Abstract We describe a combination of a statistical and symbolic approaches for automated scoring of student utterances according to their semantic content The proposed semantic classifier overcomes the limitations of bag of words methods by mapping natural language sentences into predicate representations and matching them against the automatically generated deductive closure of the domain givens buggy assumptions and domain rules With the goal to account for uncertainties in both symbolic representations of natural language sentences and logical relations between domain statements this work extends the deterministic symbolic approach by augmenting the deductive closure graph structure with conditional probabilities thus creating a Bayesian network By deriving the structure of the network formally instead of estimating it from data we alleviate the problem of sparseness of training data We compare the performance of the Bayesian network classifier with the deterministic graph matching based classifiers and baselines Keywords Dialogue based intelligent tutoring systems Bayesian networks formal methods semantic classification 1 Introduction Modern intelligent tutoring systems attempt to explore relatively unconstrained interactions with students for example via a natural language NL dialogue The rationale behind this is that allowing students to provide unrestricted input to a system would trigger meta cognitive processes that support learning i e self explaining 1 and help expose misconceptions WHY 2 ATLAS tutoring system is designed to elicit NL explanations in the domain of qualitative physics 7 The system presents a student a qualitative physics problem and asks the student to type an essay with an answer and



Access the best Study Guides, Lecture Notes and Practice Exams

Loading Unlocking...
Login

Join to view Combining Bayesian Networks and Formal Reasoning for Semantic Classification of Student Utterances and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view Combining Bayesian Networks and Formal Reasoning for Semantic Classification of Student Utterances and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?