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Baranwal, Neha
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Baranwal, N., Singh, A. & Bensch, S. (2019). Extracting Primary Objects and Spatial Relations from Sentences. In: : . Paper presented at 11th International Conference on Agents and Artificial Intelligence, Prague, Czech Republic, 19-21 February 2019..
Open this publication in new window or tab >>Extracting Primary Objects and Spatial Relations from Sentences
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In verbal human-robot interaction natural language utterances have to be grounded in visual scenes by the robot. Visual language grounding is a challenging task that includes identifying a primary object among several objects, together with the object properties and spatial relations among the objects. In this paper we focus on extracting this information from sentences only. We propose two language modelling techniques, one uses regular expressions and the other one utilizes Euclidian distance. We compare these two proposed techniques with two other techniques that utilize tree structures, namely an extended Hobb’s algorithm and an algorithm that utilizes a Stanford parse tree. A comparative analysis between all language modelling techniques shows that our proposed two approaches require less computational time than the tree-based approaches. All approaches perform good identifying the primary object and its property, but for spatial relation extraction the Stanford parse tree algorithm performs better than the other language modelling techniques. Time elapsed for the Stanford parse tree algorithm is higher than for the other techniques.

Keywords
Natural Language Grounding, Spatial Relation Extraction, Hobb’s Algorithm, Human-robot Interaction, NLTK, Google Speech, Stanford Parser
National Category
Robotics
Identifiers
urn:nbn:se:umu:diva-157635 (URN)
Conference
11th International Conference on Agents and Artificial Intelligence, Prague, Czech Republic, 19-21 February 2019.
Available from: 2019-03-26 Created: 2019-03-26 Last updated: 2019-03-29Bibliographically approved
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