DocumentCode :
3019332
Title :
Associative processes between behavioral symbols and a large scale language model
Author :
Takano, Wataru ; Nakamura, Yoshihiko
Author_Institution :
Mechano-Inf., Univ. of Tokyo, Tokyo, Japan
fYear :
2010
fDate :
3-7 May 2010
Firstpage :
2404
Lastpage :
2409
Abstract :
This paper describes an novel approach towards linguistic processing for robots through integration of a motion language model and a natural language model. The motion language model works for association of words from motion symbols. The natural language model is one used for a morphological analysis, which has been developed in natural language community. The natural language model is optimized using a enormous amount of words. So this model is scalable architecture. The motion language model and the natural language model can be integrated since both models are represented graphically. The integration of the motion language model and the natural language model allows robots not only to interpret motion patterns as sentences but also to generate motions from sentences. This paper demonstrates the validity of our proposed framework even in the case that large-scale word corpus is needed processing through experiments of interpreting motion patterns as sentences and generating motion patterns from sentences.
Keywords :
natural language processing; robots; associative process; behavioral symbols; large scale language model; large-scale word corpus; linguistic processing; morphological analysis; motion language model; natural language model; robots; Feature extraction; Hidden Markov models; Humanoid robots; Intelligent robots; Large-scale systems; Natural language processing; Natural languages; Robotics and automation; Stochastic processes; USA Councils;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics and Automation (ICRA), 2010 IEEE International Conference on
Conference_Location :
Anchorage, AK
ISSN :
1050-4729
Print_ISBN :
978-1-4244-5038-1
Electronic_ISBN :
1050-4729
Type :
conf
DOI :
10.1109/ROBOT.2010.5509532
Filename :
5509532
Link To Document :
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