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C-structures and f-structures for the British national corpus |
Wagner, Joachim; Seddah, Djamé; Foster, Jennifer; van Genabith, Josef
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We describe how the British National Corpus (BNC), a one hundred million word balanced corpus of British English, was parsed into Lexical Functional Grammar (LFG) c-structures and f-structures, using a treebank-based
parsing architecture. The parsing architecture uses a state-of-the-art statistical parser and reranker trained on the Penn Treebank to produce context-free phrase structure trees, and an annotation algorithm to automatically annotate
these trees into LFG f-structures. We describe the pre-processing steps which were taken to accommodate the differences between the Penn Treebank and the BNC. Some of the issues encountered in applying the parsing
architecture on such a large scale are discussed. The process of annotating a gold standard set of 1,000 parse trees is described. We present evaluation results obtained by evaluating the c-structures produced by the statistical parser against the c-structure gold standard. We also present the results obtained by evaluating the f-structures produced by the annotation algorithm against an
automatically constructed f-structure gold standard. The c-structures achieve an f-score of 83.7% and the f-structures an f-score of 91.2%.
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Keyword(s):
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Machine translating; lexical functional grammar |
Publication Date:
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2007 |
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Type:
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Conference item |
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Peer-Reviewed:
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Yes |
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Language(s):
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English |
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Institution:
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Dublin City University |
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Funder(s):
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Irish Research Council for Science Engineering and Technology; Science Foundation Ireland |
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Citation(s):
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Wagner, Joachim and Seddah, Djamé and Foster, Jennifer and van Genabith, Josef (2007) C-structures and f-structures for the British national corpus. In: Lexical Functional Grammar 2007, 28-30 July 2007, California, USA. |
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Publisher(s):
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CSLI Publications |
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File Format(s):
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application/pdf |
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Related Link(s):
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http://doras.dcu.ie/15205/1/jwagner_et_al_07.pdf |
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First Indexed:
2010-02-18 05:05:06 Last Updated:
2014-04-23 05:49:21 |
