Summary of the paper

Title Deep Linguistic Processing with GETARUNS for Spoken Dialogue Understanding
Authors Rodolfo Delmonte, Antonella Bristot and 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.
Topics Dialogue, Discourse annotation, representation and processing, Speech resource/database
Full paper Deep Linguistic Processing with GETARUNS for Spoken Dialogue Understanding
Slides -
Bibtex @InProceedings{DELMONTE10.383,
  author = {Rodolfo Delmonte and Antonella Bristot and Vincenzo Pallotta},
  title = {Deep Linguistic Processing with GETARUNS for Spoken Dialogue Understanding},
  booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)},
  year = {2010},
  month = {may},
  date = {19-21},
  address = {Valletta, Malta},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {2-9517408-6-7},
  language = {english}
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