A novel framework of training hidden Markov support vector machines from lightly-annotated data

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Natural language understanding (NLU) aims to map sentences to their semantic mean representations. Statistical approaches to NLU normally require fully-annotated training data where each sentence is paired with its word-level semantic annotations. In this paper, we propose a novel learning framework which trains the Hidden Markov Support Vector Machines (HM-SVMs) without the use of expensive fully-annotated data. In particular, our learning approach takes as input a training set of sentences labeled with abstract semantic annotations encoding underlying embedded structural relations and automatically induces derivation rules that map sentences to their semantic meaning representations. The proposed approach has been tested on the DARPA Communicator Data and achieved 93.18% in F-measure, which outperforms the previously proposed approaches of training the hidden vector state model or conditional random fields from unaligned data, with a relative error reduction rate of 43.3% and 10.6% being achieved.

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Publication date2011
Publication titleProceeding : CIKM '11 proceedings of the 20th ACM international conference on Information and knowledge management
EditorsBettina Berendt, Arjen de Vries, Wenfei Fan, Craig Macdonald, Iadh Ounis, Ian Ruthven
Place of PublicationNew York (US)
Number of pages4
ISBN (Print)978-1-4503-0717-8
Original languageEnglish
Event20th ACM international conference on Information and knowledge management, CIKM '11 - Glasgow, United Kingdom
Duration: 24 Oct 201128 Oct 2011


Conference20th ACM international conference on Information and knowledge management, CIKM '11
CountryUnited Kingdom


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