Context Model Acquisition from Spoken Utterances

• Published in: International journal of software engineering and knowledge engineering Vol. 27; no. 9n10; pp. 1439 - 1453
• Main Authors: Weigelt, Sebastian, Hey, Tobias, Tichy, Walter F.
• Format: Journal Article
• Language: English
• Published: Singapore World Scientific Publishing Company 01.12.2017; World Scientific Publishing Co. Pte., Ltd
• Subjects: Automatic speech recognition; Feasibility studies; natural language understanding; spoken language understanding; natural language processing; knowledge representation; context model; context; language model; programming in natural language; Spoken language interfaces; end-user programming; ontologies
• ISSN: 0218-1940; 1793-6403
• DOI: 10.1142/S0218194017400058

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 rule-based (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 F 1 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 investigate whether our context model is useful for other language understanding tasks, e.g. anaphora resolution, topic analysis, or correction of automatic speech recognition errors.