Never look back: an alternative to centering
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Support Vector Learning for Semantic Argument Classification
Machine Learning
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Computational Linguistics
Marker-Passing inference in the scone knowledge-base system
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
Generating Instruction Automatically for the Reading Strategy of Self-Questioning
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Recognizing Young Readers' Spoken Questions
International Journal of Artificial Intelligence in Education
Hi-index | 0.00 |
Understanding mental states in narratives is an important aspect of human language comprehension. By "mental states" we refer to beliefs, states of knowledge, points of view, and suppositions, all of which may change over time. In this paper, we propose an approach for automatically extracting and understanding multiple mental states in stories. Our model consists of two parts: (1) a parser that takes an English sentence and translates it to some semantic operations; (2) a mental-state inference engine that reads in the semantic operations and produces a situation model that represents the meaning of the sentence. We present the performance of the system on a corpus of children stories containing both fictional and non-fictional texts.