Information extraction as a stepping stone toward story understanding
Understanding language understanding
Finding the WRITE Stuff: Automatic Identification of Discourse Structure in Student Essays
IEEE Intelligent Systems
ITS, Agents, BDI, and Affection: Trying to Make a Plan Come Together
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Feedback on Children's Stories via Multiple Interface Agents
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Story understanding through multi-representation model construction
HLT-NAACL-TEXTMEANING '03 Proceedings of the HLT-NAACL 2003 workshop on Text meaning - Volume 9
Wide-coverage semantic representations from a CCG parser
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Merging stories with shallow semantics
KRAQ '06 Proceedings of the Workshop KRAQ'06 on Knowledge and Reasoning for Language Processing
Modeling reciprocity in social interactions with probabilistic latent space models
Natural Language Engineering
| Hi-index | 0.00 |
In this paper we present how the automatic extraction of events from text can be used to both classify narrative texts according to plot quality and produce advice in an interactive learning environment intended to help students with story writing. We focus on the story rewriting task, in which an exemplar story is read to the students and the students rewrite the story in their own words. The system automatically extracts events from the raw text, formalized as a sequence of temporally ordered predicate-arguments. These events are given to a machine-learner that produces a coarse-grained rating of the story. The results of the machine-learner and the extracted events are then used to generate fine-grained advice for the students.