Abstract
This paper proposes a method to help robots understand object semantics. The method presented in this paper can enhance robot’s performance and efficiency while working with ambiguous instructions to interact with unfamiliar objects. Specifically, the proposed method can reduce the complexity of assigning the functions, properties or other characteristics for each object which robot may interact within a social environment. The method assists the robot to comprehend the scene based on semantics analysis of the dictionary definition. The proposed semantics comprehension method includes the comprehension of dictionary definitions, the formulation of logic representation, and the generation of natural-language descriptions. The applicability of the approach has been demonstrated. The model performance has been evaluated based on precision, recall, and f-score. Both logic representation formulation results and natural language representation results have been displayed.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation. OSDI 2016, pp. 265–283. USENIX Association, Berkeley (2016). http://dl.acm.org/citation.cfm?id=3026877.3026899
Breazeal, C.: Role of expressive behaviour for robots that learn from people. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 364(1535), 3527–3538 (2009)
Kamei, K., Zanlungo, F., Kanda, T., Horikawa, Y., Miyashita, T., Hagita, N.: Cloud networked robotics for social robotic services extending robotic functional service standards to support autonomous mobility system in social environments. In: 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 897–902. IEEE (2017)
Maedche, A., Staab, S.: Ontology learning for the semantic web. IEEE Intell. Syst. 16(2), 72–79 (2001)
McGuinness, D.L., Van Harmelen, F., et al.: Owl web ontology language overview. W3C Recomm. 10(10), 5–6 (2004)
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Oxford University Press: Oxford Dictionary of English. Oxford University Press, Oxford (2010)
Ramanathan, V., et al.: Learning semantic relationships for better action retrieval in images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1100–1109 (2015)
Scassellati, B., Admoni, H., Matarić, M.: Robots for use in autism research. Annu. Rev. Biomed. Eng. 14, 275–294 (2012)
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480–1489 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Yan, F., Zhang, Y., He, H. (2018). Semantics Comprehension of Entities in Dictionary Corpora for Robot Scene Understanding. In: Ge, S., et al. Social Robotics. ICSR 2018. Lecture Notes in Computer Science(), vol 11357. Springer, Cham. https://doi.org/10.1007/978-3-030-05204-1_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-05204-1_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05203-4
Online ISBN: 978-3-030-05204-1
eBook Packages: Computer ScienceComputer Science (R0)