Stochastically-Based Semantic Analysis
Stochastically-Based Semantic Analysis
Parsing engineering and empirical robustness
Natural Language Engineering
Robustness beyond shallowness: incremental deep parsing
Natural Language Engineering
Robust grammatical analysis for spoken dialogue systems
Natural Language Engineering
Automatic construction of frame representations for spontaneous speech in unrestricted domains
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
2005 Special Issue: Challenges in real-life emotion annotation and machine learning based detection
Neural Networks - Special issue: Emotion and brain
Generic parsing for multi-domain semantic interpretation
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
TSD'10 Proceedings of the 13th international conference on Text, speech and dialogue
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LOGUS is a French-speaking spoken language understanding (SLU) system which carries out a deeper analysis than those achieved by standard concept spotters. It is designed for multi-domain conversational systems or for systems that are working on complex application domains. Based on a logical approach, the system adapts the ideas of incremental robust parsing to the issue of SLU. The paper provides a detailed description of the system as well as results from two evaluation campaigns that concerned all of current French-speaking SLU systems. The observed error rates suggest that our logical approach can stand comparison with concept spotters on restricted application domains, but also that its behaviour is promising for larger domains. The question of the generality of the approach is precisely addressed by our current investigations on a new task: SLU for an emotional robot companion for young hospital patents.