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Closing the gap between stochastic and rule-based LFG grammars
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In: Hautli, Annette, Cetinoglu, Ozlem and van Genabith, Josef orcid:0000-0003-1322-7944 (2010) Closing the gap between stochastic and rule-based LFG grammars. In: the LFG10 Conference, 18-20 July 2010, Ottowa, Canada. (2010)
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Dependency parsing resources for French: Converting acquired lexical functional grammar F-Structure annotations and parsing F-Structures directly
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In: Schluter, Natalie and van Genabith, Josef orcid:0000-0003-1322-7944 (2009) Dependency parsing resources for French: Converting acquired lexical functional grammar F-Structure annotations and parsing F-Structures directly. In: Nodalida 2009 Conference, 14 - 16 May 2009, Odense, Denmark. (2009)
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Treebank-based acquisition of LFG parsing resources for French
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In: Schluter, Natalie and van Genabith, Josef (2008) Treebank-based acquisition of LFG parsing resources for French. In: the Sixth International Language Resources and Evaluation Conference (LREC'08), May 28-30, 2008, Marrakech, Morocco. (2008)
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Packed rules for automatic transfer-rule induction
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In: Graham, Yvette and van Genabith, Josef (2008) Packed rules for automatic transfer-rule induction. In: the European Association of Machine Translation Conference 2008, Hamburg, Germany. (2008)
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German particle verbs and pleonastic prepositions
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In: Rehbein, Ines and van Genabith, Josef (2006) German particle verbs and pleonastic prepositions. In: Third ACL-SIGSEM Workshop on Prepositions, 3 April 2006, Trento, Italy. (2006)
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Robust PCFG-based generation using automatically acquired LFG approximations
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In: Cahill, Aoife orcid:0000-0002-3519-7726 and van Genabith, Josef (2006) Robust PCFG-based generation using automatically acquired LFG approximations. In: COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, 17-21 July 2006, Sydney, Australia. (2006)
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Automatic extraction of large-scale multilingual lexical resources
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In: O'Donovan, Ruth (2006) Automatic extraction of large-scale multilingual lexical resources. PhD thesis, Dublin City University. (2006)
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Automatic treebank annotation for the acquisition of LFG resources
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In: Burke, Michael (2006) Automatic treebank annotation for the acquisition of LFG resources. PhD thesis, Dublin City University. (2006)
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Parsing with automatically acquired, wide-coverage, robust, probabilistic LFG approximations
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Cahill, Aoife. - : Dublin City University. School of Computing, 2004
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In: Cahill, Aoife orcid:0000-0002-3519-7726 (2004) Parsing with automatically acquired, wide-coverage, robust, probabilistic LFG approximations. PhD thesis, Dublin City University. (2004)
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Abstract:
Traditionally, rich, constraint-based grammatical resources have been hand-coded. Scaling such resources beyond toy fragments to unrestricted, real text is knowledge-intensive, timeconsuming and expensive. The work reported in this thesis is part of a larger project to automate as much as possible the construction of wide-coverage, deep, constraint-based grammatical resources from treebanks. The Penn-II treebank is a large collection of parse-annotated newspaper text. We have designed a Lexical-Functional Grammar (LFG) (Kaplan and Bresnan, 1982) f-structure annotation algorithm to automatically annotate this treebank with f-structure information approximating to basic predicate-argument or dependency structures (Cahill et al., 2002c, 2004a). We then use the f-structure-annotated treebank resource to automatically extract grammars and lexical resources for parsing new text into f-structures. We have designed and implemented the Treebank Tool Suite (TTS) to support the linguistic work that seeds the automatic f-structure annotation algorithm (Cahill and van Genabith, 2002) and the F-Structure Annotation Tool (FSAT) to validate and visualise the results of automatic f-structure annotation. We have designed and implemented two PCFG-based probabilistic parsing architectures for parsing unseen text into f-structures: the pipeline and the integrated model. Both architectures parse raw text into basic, but possibly incomplete, predicate-argument structures (“proto f-structures”) with long distance dependencies (LDDs) unresolved (Cahill et al., 2002c). We have designed and implemented a method for automatically resolving LDDs at f-structure level based on a finite approximation of functional uncertainty equations (Kaplan and Zaenen, 1989) automatically acquired from the f structure-annotated treebank resource (Cahill et al., 2004b). To date, the best result achieved by our own Penn-II induced grammars is a dependency f-score of 80.33% against the PARC 700, an improvement of 0.73% over the best handcrafted grammar of (Kaplan et al., 2004). The processing architecture developed in this thesis is highly flexible: using external, state-of-the-art parsing technologies (Charniak, 2000) in our pipeline model, we achieve a dependency f-score of 81.79% against the PARC 700, an improvement of 2.19% over the results reported in Kaplan et al. (2004). We have also ported our grammar induction methodology to German and the TIGER treebank resource (Cahill et al., 2003a). We have developed a method for treebank-based, wide-coverage, deep, constraintbased grammar acquisition. The resulting PCFG-based LFG approximations parse the Penn-II treebank with wider coverage (measured in terms of complete spanning parse) and parsing results comparable to or better than those achieved by the best hand-crafted grammars, with, we believe, considerably less grammar development effort. We believe that our approach successfully addresses the knowledge-acquisition bottleneck (familiar from rule-based approaches to Al and NLP) in wide-coverage, constraint-based grammar development. Our approach can provide an attractive, wide-coverage, multilingual, deep, constraint-based grammar acquisition paradigm.
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Keyword:
constraint-based grammatical resources; Lexical-Functional Grammar; LFG; Machine translating; Treebank Tool Suite; TTS
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URL: http://doras.dcu.ie/17234/
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