Deep Linguistic Processing with GETARUNS for Spoken Dialogue Understanding
نویسندگان
چکیده
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.
منابع مشابه
Deep Linguistic Processing for Spoken Dialogue Systems
We describe a framework for deep linguistic processing for natural language understanding in task-oriented spoken dialogue systems. The goal is to create domaingeneral processing techniques that can be shared across all domains and dialogue tasks, combined with domain-specific optimization based on an ontology mapping from the generic LF to the application ontology. This framework has been test...
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