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Dialog-Based Meaning Derivation Service for Technical Language Domains

Wachtel, Alexander; Schulz, Sophie; Tichy, Walter F.

Abstract:
In our previous work, we present on how new algorithms
can be recognized and learned from human descriptions.
In this case, end users are able to extend the given system by
their own functionality. During the evaluation, due to language
limits on the system, it could interpret only 59% of user input
correctly. In this paper, we provide an approach on the Dialogbased
Meaning Derivation Service (DMDS). In case, user input
does not match to the system knowledge, DMDS serves various
word networks to find relevant synonyms as candidates for the
unknown word. DMDS then tries to verify these candidates
to the given knowledge base. Finally, matched candidates are
presented to the end user by the dialog system for confirmation
of a contextual match. The meaning is learned after the user
confirmation and is mapped to the given functionality, and can
be used afterwards. Therefore, the model developed in this work
can be categorized as supervised learning. Finally, both the
performance and the quality recorded by input from a user
study were examined. However, DMDS improves the correct
interpretation of the system from 59% to 82%. ... mehr



Originalveröffentlichung
DOI: 10.1109/ICOSC.2019.8665513
Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Proceedingsbeitrag
Jahr 2019
Sprache Englisch
Identifikator ISBN: 978-1-5386-6783-5
ISSN: 2325-6516
KITopen-ID: 1000094120
Erschienen in IEEE 13th International Conference on Semantic Computing (ICSC), Newport Beach; CA, United States; 30 January - 1 February 2019
Verlag IEEE, Piscataway (NJ)
Seiten 375–380
Bemerkung zur Veröffentlichung Article number 8665513
Schlagworte Dialog Systems; Dialog-based Meaning Derivation; Knowledge Base; Programming in natural language
Nachgewiesen in Scopus
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