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dc.contributor.authorPerera, Ren_NZ
dc.contributor.authorNand, Pen_NZ
dc.contributor.authorNaeem, Aen_NZ
dc.date.accessioned2017-07-26T22:09:44Z
dc.date.available2017-07-26T22:09:44Z
dc.date.copyright2017-06en_NZ
dc.identifier.citationProgress in Artificial Intelligence, 6(2), 105-119.
dc.identifier.issn2192-6352en_NZ
dc.identifier.issn2192-6360en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/10690
dc.description.abstractQuestion Answering over Linked Data (QALD) refer to the use of Linked Data by question answering systems, and in recent times this has become increasingly popular as it opens up a massive Linked Data cloud which is a rich source of encoded knowledge. However, a major shortfall of current QALD systems is that they focus on presenting a single fact or factoid answer which is derived using SPARQL (SPARQL Protocol and RDF Query Language) queries. There is now an increased interest in development of human-like systems which would be able to answer questions and even hold conversations by constructing sentences akin to humans. In this paper, we introduce a new answer construction and presentation system, which utilizes the linguistic structure of the source question and the factoid answer to construct an answer sentence which closely emanates a human-generated answer. We employ both semantic Web technology and the linguistic structure to construct the answer sentences. The core of the research resides on extracting dependency subtree patterns from the questions and utilizing them in conjunction with the factoid answer to generate the answer sentence with a natural feel akin to an answer from a human when asked the question. We evaluated the system for both linguistic accuracy and naturalness using human evaluation. These evaluation processes showed that the proposed approach is able to generate answer sentences which have linguistic accuracy and natural readability quotients of more than 70%. In addition, we also carried out a feasibility analysis on using automatic metrics for answer sentence evaluation. The results from this phase showed that the there is not a strong correlation between the results from automatic metric evaluation and the human ratings of the machine-generated answers.
dc.publisherSpringer
dc.relation.urihttps://link.springer.com/article/10.1007%2Fs13748-017-0113-9
dc.rightsAn author may self-archive an author-created version of his/her article on his/her own website and or in his/her institutional repository. He/she may also deposit this version on his/her funder’s or funder’s designated repository at the funder’s request or as a result of a legal obligation, provided it is not made publicly available until 12 months after official publication. He/ she may not use the publisher's PDF version, which is posted on www.springerlink.com, for the purpose of self-archiving or deposit. Furthermore, the author may only post his/her version provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at www.springerlink.com”. (Please also see Publisher’s Version and Citation)
dc.subjectAnswer presentation; Question answering; Dependency parsing; Linked data; Semantic web
dc.titleUtilizing Typed Dependency Subtree Patterns for Answer Sentence Generation in Question Answering Systemsen_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.1007/s13748-017-0113-9en_NZ
aut.relation.endpage119
aut.relation.issue2en_NZ
aut.relation.startpage105
aut.relation.volume6en_NZ
pubs.elements-id280728
aut.relation.journalProgress in Artificial Intelligenceen_NZ


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