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

Zhou, Deyu and He, Yulan (2011). A novel framework of training hidden Markov support vector machines from lightly-annotated data. In: 20th ACM Conference on Information and Knowledge Management (CIKM 2011), 24 - 28 Oct 2011, Glasgow, UK.

DOI (Digital Object Identifier) Link: http://dx.doi.org/10.1145/2063576.2063881
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Abstract

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.

Item Type: Conference Item
Copyright Holders: 2011 ACM
Extra Information: CIKM '11 Proceedings of the 20th ACM International Conference on Information and Knowledge Management
ACM New York, NY, USA ©2011
ISBN: 978-1-4503-0717-8
Keywords: hidden Markov support vector machines (HM-SVMs); natural language understanding; semantic parsing; algorithms; experimentation
Academic Unit/Department: Knowledge Media Institute
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 29460
Depositing User: Kay Dave
Date Deposited: 18 Nov 2011 10:38
Last Modified: 25 Jun 2014 13:38
URI: http://oro.open.ac.uk/id/eprint/29460
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