Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Discriminative Reranking for Natural Language Parsing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Discriminative reranking for semantic parsing
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Persuading users through counseling dialogue with a conversational agent
Proceedings of the 4th International Conference on Persuasive Technology
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Reranking modules of conventional parsers make use of either probabilistic weights linked to the production rules or just hand crafted rules to choose the best possible parse. Other proposals make use of the topology of the parse trees and lexical features to reorder the parsing results. In this work, a new reranking approach is presented. There are two main novelties introduced in this paper: firstly, a new discriminative reranking method of parsing results has been applied using Conditional Random Fields (CRFs) for sequence tagging. Secondly, a mixture of syntactic and semantic features, specifically designed for Embodied Conversational Agents (ECAs) interactions, has been used. This approach has been trained with a Corpus of over 4,000 dialogues, obtained from real interactions of real users with an online ECA. Results show that this approach provides a significant improvement over the parsing results of out-of-domain sentences; that is, sentences for which there is no optimal parse among the candidates given by the baseline parse.