Supertagging: an approach to almost parsing
Computational Linguistics
A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Gemini: a natural language system for spoken-language understanding
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Targeted help for spoken dialogue systems: intelligent feedback improves naive users' performance
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Distributional phrase structure induction
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Wide-coverage semantic representations from a CCG parser
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Common sense data acquisition for indoor mobile robots
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
The "casual cashmere diaper bag": constraining speech recognition using examples
ISDS '97 Interactive Spoken Dialog Systems on Bringing Speech and NLP Together in Real Applications
Natural language grammar induction with a generative constituent-context model
Pattern Recognition
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In this paper, we discuss the use of Open Mind Indoor Common Sense (OMICS) project for the purpose of speech recognition of user requests. As part of OMICS data collection, we asked users to enter different ways of asking a robot to perform specific tasks. This paraphrasing data is processed using Natural Language techniques and lexical resources like WordNet to generate a Finite State Grammar Transducer (FSGT). This transducer captures the variations in user requests and captures their structure. We compare the task recognition performance of this FSGT model with an n-gram Statistical Language Model (SLM). The SLM model is trained with the same data that was used to generate the FSGT. The FSGT model and SLM are combined in a two-pass system to optimize full and partial recognition for both in-grammar and out-of-grammar user requests. Our work validates the use of a web based knowledge capture system to harvest phrases to build grammar models. Work was performed using Nuance Speech Recognition system.