A statistical approach to a verb vector task classifier
| dc.contributor.author | Jiang, ZiPeng | en |
| dc.date.accessioned | 2015-02-10T13:12:55Z | |
| dc.date.available | 2015-02-10T13:12:55Z | |
| dc.date.issued | 2010-11 | |
| dc.identifier.citation | Jiang, Z. (2010) 'A statistical approach to a verb vector task classifier'. MSc by research thesis. University of Bedfordshire. | en |
| dc.identifier.uri | http://hdl.handle.net/10547/344339 | |
| dc.description | A thesis submitted to the University of Bedfordshire, in fulfilment ofthe requirements for the degree of Master of Science by research | en |
| dc.description.abstract | How to enable a service robot to understand its user's intention is a hot topic of research today. Based on its understanding, the robot can coordinate and adjust its behaviours to provide desired assistance and services to the user as a capable partner. Active Robot Learning (ARL) is an approach to the development of the understanding of human intention. The task action bank is part of the ARL which can store task categories. In this approach, a robot actively performs test actions in order to obtain its user's intention from the user's response to the action. This thesis presents an approach to verbs clustering based on the basic action required of the robot, using a statistical method. A parser is established to process a corpus and analyse the probability of the verb feature vector, for example when the user says "bring me a cup of coffee", this means the same as "give me a cup of coffee". This parser could identify similar verbs between "bring" and "give" with the statistical method. Experimental results show the collocation between semantically related verbs, which can be further utilised to establish a test action bank for Active Robot Learning (ARL). | |
| dc.language.iso | en | en |
| dc.publisher | University of Bedfordshire | en |
| dc.subject | H671 Robotics | en |
| dc.subject | robotics | en |
| dc.subject | human-robot interaction | en |
| dc.subject | service robots | en |
| dc.subject | active robot learning | en |
| dc.title | A statistical approach to a verb vector task classifier | en |
| dc.type | Thesis or dissertation | en |
| refterms.dateFOA | 2020-05-14T06:35:15Z | |
| html.description.abstract | How to enable a service robot to understand its user's intention is a hot topic of research today. Based on its understanding, the robot can coordinate and adjust its behaviours to provide desired assistance and services to the user as a capable partner. Active Robot Learning (ARL) is an approach to the development of the understanding of human intention. The task action bank is part of the ARL which can store task categories. In this approach, a robot actively performs test actions in order to obtain its user's intention from the user's response to the action. This thesis presents an approach to verbs clustering based on the basic action required of the robot, using a statistical method. A parser is established to process a corpus and analyse the probability of the verb feature vector, for example when the user says "bring me a cup of coffee", this means the same as "give me a cup of coffee". This parser could identify similar verbs between "bring" and "give" with the statistical method. Experimental results show the collocation between semantically related verbs, which can be further utilised to establish a test action bank for Active Robot Learning (ARL). |
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