A statistical approach to a verb vector task classifier

2.50
Hdl Handle:
http://hdl.handle.net/10547/344339
Title:
A statistical approach to a verb vector task classifier
Authors:
Jiang, ZiPeng
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).
Citation:
Jiang, Z. (2010) 'A statistical approach to a verb vector task classifier'. MSc by research thesis. University of Bedfordshire.
Publisher:
University of Bedfordshire
Issue Date:
Nov-2010
URI:
http://hdl.handle.net/10547/344339
Type:
Thesis or dissertation
Language:
en
Description:
A thesis submitted to the University of Bedfordshire, in fulfilment ofthe requirements for the degree of Master of Science by research
Appears in Collections:
Masters e-theses

Full metadata record

DC FieldValue Language
dc.contributor.authorJiang, ZiPengen
dc.date.accessioned2015-02-10T13:12:55Z-
dc.date.available2015-02-10T13:12:55Z-
dc.date.issued2010-11-
dc.identifier.citationJiang, Z. (2010) 'A statistical approach to a verb vector task classifier'. MSc by research thesis. University of Bedfordshire.en
dc.identifier.urihttp://hdl.handle.net/10547/344339-
dc.descriptionA thesis submitted to the University of Bedfordshire, in fulfilment ofthe requirements for the degree of Master of Science by researchen
dc.description.abstractHow 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).en
dc.language.isoenen
dc.publisherUniversity of Bedfordshireen
dc.subjectH671 Roboticsen
dc.subjectroboticsen
dc.subjecthuman-robot interactionen
dc.subjectservice robotsen
dc.subjectactive robot learningen
dc.titleA statistical approach to a verb vector task classifieren
dc.typeThesis or dissertationen
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