Weakly supervised semantic frame induction: effects of using background knowledge

TitleWeakly supervised semantic frame induction: effects of using background knowledge
Publication TypeTalks
Authorsvan de Loo, J., Gemmeke J. F., De Pauw G., Daelemans W., & Van Damme H.
Place PresentedPresented at the 24th Meeting of Computational Linguistics in the Netherlands (CLIN 2014), Leiden, The Netherlands
Year of Publication2014
Date Presented17/01/2014
Abstract

In previously reported research, we developed a framework for weakly supervised semantic frame induction, based on hierarchical hidden Markov models. We now present results of a detailed analysis of the inner workings of the framework, and the effect of incorporating background knowledge in the models. The semantic frame induction system was designed to be used in an assistive vocal interface for people with physical impairments, which is specifically trained to adapt itself to each individual user (project ‘ALADIN’). The system’s task is to induce frame-based semantic representations of spoken or written utterances, based on a training set of utterances and associated semantic frames. The weak supervision lies in the fact that there is only supervision at the utterance level; no relations between parts of the utterances and parts of the semantic frames are specified in advance. We show how the incorporation of background knowledge in the system can facilitate and accelerate the process of learning the relations between structures in the commands and in the semantic frames. The experimental results presented here were produced with textual command input.