Deep Linguistic Processing with GETARUNS for Spoken Dialogue Understanding

Rodolfo Delmonte, Antonella Bristot, Vincenzo Pallotta

Abstract
In this paper we will present work carried out to scale up the system for text understanding called GETARUNS, and port it to be used in dialogue understanding. The current goal is that of extracting automatically argumentative information in order to build argumentative structure. The long term goal is using argumentative structure to produce automatic summarization of spoken dialogues. Very much like other deep linguistic processing systems, our system is a generic text/dialogue understanding system that can be used in connection with an ontology ― WordNet - and other similar repositories of commonsense knowledge. We will present the adjustments we made in order to cope with transcribed spoken dialogues like those produced in the ICSI Berkeley project. In a final section we present preliminary evaluation of the system on two tasks: the task of automatic argumentative labeling and another frequently addressed task: referential vs. non-referential pronominal detection. Results obtained fair much higher than those reported in similar experiments with machine learning approaches.
Anthology ID:
L10-1261
Volume:
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)
Month:
May
Year:
2010
Address:
Valletta, Malta
Editors:
Nicoletta Calzolari, Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Mike Rosner, Daniel Tapias
Venue:
LREC
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Publisher:
European Language Resources Association (ELRA)
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URL:
http://www.lrec-conf.org/proceedings/lrec2010/pdf/383_Paper.pdf
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Cite (ACL):
Rodolfo Delmonte, Antonella Bristot, and Vincenzo Pallotta. 2010. Deep Linguistic Processing with GETARUNS for Spoken Dialogue Understanding. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).
Cite (Informal):
Deep Linguistic Processing with GETARUNS for Spoken Dialogue Understanding (Delmonte et al., LREC 2010)
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http://www.lrec-conf.org/proceedings/lrec2010/pdf/383_Paper.pdf
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