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Volume 4 Issue 4
Oct.  2017

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2017  >  4(4): 686-695
Dongcai Lu and Xiaoping Chen, "Interpreting and Extracting Open Knowledge for Human-Robot Interaction," IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 686-695, Oct. 2017. doi: 10.1109/JAS.2017.7510628
Citation: Dongcai Lu and Xiaoping Chen, "Interpreting and Extracting Open Knowledge for Human-Robot Interaction," IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 686-695, Oct. 2017. doi: 10.1109/JAS.2017.7510628
shu

Interpreting and Extracting Open Knowledge for Human-Robot Interaction

doi: 10.1109/JAS.2017.7510628

  • School of Science and Technology, University of Science and Technology of China, Hefei, 230027 China

Funds:

the National Natural Science Foundation of China 61175057

the USTC Key-Direction Research Fund WK0110000028

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    Abstract
  • A more natural way for non-expert users to express their tasks in an open-ended set is to use natural language. In this case, a human-centered intelligent agent/robot is required to be able to understand and generate plans for these naturally expressed tasks. For this purpose, it is a good way to enhance intelligent robot's abilities by utilizing open knowledge extracted from the web, instead of hand-coded knowledge. A key challenge of utilizing open knowledge lies in the semantic interpretation of the open knowledge organized in multiple modes, which can be unstructured or semi-structured, before one can use it. Previous approaches used a limited lexicon to employ combinatory categorial grammar (CCG) as the underlying formalism for semantic parsing over sentences. Here, we propose a more effective learning method to interpret semi-structured user instructions. Moreover, we present a new heuristic method to recover missing semantic information from the context of an instruction. Experiments showed that the proposed approach renders significant performance improvement compared to the baseline methods and the recovering method is promising.

     

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