Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Discourse and Information Structure
Journal of Logic, Language and Information
Robust textual inference via graph matching
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Automatic short answer marking
EdAppsNLP 05 Proceedings of the second workshop on Building Educational Applications Using NLP
A phrase-based alignment model for natural language inference
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Recognizing entailment in intelligent tutoring systems*
Natural Language Engineering
Diagnosing meaning errors in short answers to reading comprehension questions
EANL '08 Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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
TIWTE '11 Proceedings of the TextInfer 2011 Workshop on Textual Entailment
Short answer assessment: establishing links between research strands
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Short answer assessment: establishing links between research strands
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
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There is a rise in interest in the evaluation of meaning in real-life applications, e.g., for assessing the content of short answers. The approaches typically use a combination of shallow and deep representations, but little use is made of the semantic formalisms created by theoretical linguists to represent meaning. In this paper, we explore the use of the underspecified semantic formalism LRS, which combines the capability of precisely representing semantic distinctions with the robustness and modularity needed to represent meaning in real-life applications. We show that a content-assessment approach built on LRS outperforms a previous approach on the CREG data set, a freely available corpus of answers to reading comprehension exercises by learners of German. The use of such a formalism also readily supports the integration of notions building on semantic distinctions, such as the information structuring in discourse, which we show to be useful for content assessment.