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Context Model Acquisition from Spoken Utterances

Weigelt, Sebastian; Hey, Tobias; Tichy, Walter F.

Current systems with spoken language interfaces do not leverage contextual information. Therefore, they struggle with understanding speakers’ intentions. We propose a system that creates a context model from user utterances to overcome this lack of information. It comprises eight types of contextual information organized in three layers: individual, conceptual, and hierarchical. We have implemented our approach as a part of the project PARSE. It aims at enabling laypersons to construct simple programs by dialog. Our implementation incrementally generates context including occurring entities and actions as well as their conceptualizations, state transitions, and other types of contextual information. Its analyses are knowledge- or rulebased (depending on the context type), but we make use of many well-known probabilistic NLP techniques. In a user study we have shown the feasibility of our approach, achieving F1 scores from 72% up to 98% depending on the type of contextual information. The context model enables us to resolve complex identity relations. However, quantifying this effect is subject to future work. Likewise, we plan to in ... mehr

Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Proceedingsbeitrag
Jahr 2017
Sprache Englisch
Identifikator DOI: 10.18293/SEKE2017-083
KITopen ID: 1000071602
Erschienen in SEKE 2017. The 29th International Conference on Software Engineering & Knowledge Engineering, Pittsburgh, PA, July 5 - 7, 2017
Verlag Wyndham Pittsburgh University Center, Pittsburgh, PA
Seiten 201-206
Schlagworte Spoken Language Understanding, Natural Language Understanding, context model, Natural Language Processing, Knowledge Engineering, Knowledge Representation, Discourse Analysis, Conceptualization
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