The Experts below are selected from a list of 246 Experts worldwide ranked by ideXlab platform
Benoit Crabbe - One of the best experts on this subject based on the ideXlab platform.
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multilingual discriminative lexicalized Phrase Structure parsing
Empirical Methods in Natural Language Processing, 2015Co-Authors: Benoit CrabbeAbstract:We provide a generalization of discriminative lexicalized shift reduce parsing techniques for Phrase Structure grammar to a wide range of morphologically rich languages. The model is efficient and outperforms recent strong baselines on almost all languages considered. It takes advantage of a dependency based modelling of morphology and a shallow modelling of constituency boundaries.
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an lr inspired generalized lexicalized Phrase Structure parser
International Conference on Computational Linguistics, 2014Co-Authors: Benoit CrabbeAbstract:The paper introduces an LR-based algorithm for efficient Phrase Structure parsing of morphologically rich languages. The algorithm generalizes lexicalized parsing (Collins, 2003) by allowing a Structured representation of the lexical items. Together with a discriminative weighting component (Collins, 2002), we show that this representation allows us to achieve state of the art accurracy results on a morphologically rich language such as French while achieving more efficient parsing times than the state of the art parsers on the French data set. A comparison with English, a lexically poor language, is also provided.
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Multilingual discriminative shift reduce Phrase Structure parsing for the SPMRL 2014 shared task
2014Co-Authors: Benoit Crabbe, Djamé SeddahAbstract:This paper describes the design of a multilingual lexicalized discriminative shift reduce Phrase Structure based parser used to parse the SPMRL 2014 shared task data set. It reports the results of one of the first massively multilingual lexicalized Phrase Structure parser and shows that it behaves surprisingly well on a multilingual setting.
Joakim Nivre - One of the best experts on this subject based on the ideXlab platform.
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parsing discontinuous Phrase Structure with grammatical functions
International conference natural language processing, 2008Co-Authors: Johan Hall, Joakim NivreAbstract:This paper presents a novel technique for parsing discontinuous Phrase Structure representations, labeled with both Phrase labels and grammatical functions. Phrase Structure representations are transformed into dependency representations with complex edge labels, which makes it possible to induce a dependency parser model that recovers the Phrase Structure with both Phrase labels and grammatical functions. We perform an evaluation on the German TIGER treebank and the Swedish Talbanken05 treebank and report competitive results for both data sets.
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GoTAL - Parsing Discontinuous Phrase Structure with Grammatical Functions
Advances in Natural Language Processing, 2008Co-Authors: Johan Hall, Joakim NivreAbstract:This paper presents a novel technique for parsing discontinuous Phrase Structure representations, labeled with both Phrase labels and grammatical functions. Phrase Structure representations are transformed into dependency representations with complex edge labels, which makes it possible to induce a dependency parser model that recovers the Phrase Structure with both Phrase labels and grammatical functions. We perform an evaluation on the German TIGER treebank and the Swedish Talbanken05 treebank and report competitive results for both data sets.
Chengqing Zong - One of the best experts on this subject based on the ideXlab platform.
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Phrase Structure parsing with dependency Structure
International Conference on Computational Linguistics, 2010Co-Authors: Zhiguo Wang, Chengqing ZongAbstract:In this paper we present a novel Phrase Structure parsing approach with the help of dependency Structure. Different with existing Phrase parsers, in our approach the inference procedure is guided by dependency Structure, which makes the parsing procedure flexibly. The experimental results show our approach is much more accurate. With the help of golden dependency trees, F1 score of our parser achieves 96.08% on Penn English Treebank and 90.61% on Penn Chinese Treebank. With the help of N-best dependency trees generated by modified MSTParser, F1 score achieves 90.54% for English and 83.93% for Chinese.
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An approach to automatic acquisition of translation templates based on Phrase Structure extraction and alignment
IEEE Transactions on Audio Speech and Language Processing, 2006Co-Authors: Chengqing ZongAbstract:In this paper, we propose a new approach for automatically acquiring translation templates from unannotated bilingual spoken language corpora. Two basic algorithms are adopted: a grammar induction algorithm, and an alignment algorithm using bracketing transduction grammar. The approach is unsupervised, statistical, and data-driven, and employs no parsing procedure. The acquisition procedure consists of two steps. First, semantic groups and Phrase Structure groups are extracted from both the source language and the target language. Second, an alignment algorithm based on bracketing transduction grammar aligns the Phrase Structure groups. The aligned Phrase Structure groups are post-processed, yielding translation templates. Preliminary experimental results show that the algorithm is effective
Bernd Bohnet - One of the best experts on this subject based on the ideXlab platform.
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stacking of dependency and Phrase Structure parsers
International Conference on Computational Linguistics, 2012Co-Authors: Richard Farkas, Bernd BohnetAbstract:We investigate the stacking of dependency and Phrase Structure parsers, i.e. we define features from the output of a Phrase Structure parser for a dependency parser and vice versa. Our features are based on the original form of the external parses and we also compare this approach to converting Phrase Structures to dependencies then applying standard stacking on the converted output. The proposed method provides high accuracy gains for both Phrase Structure and dependency parsing. With the features derived from the Phrase Structures, we achieved a gain of 0.89 percentage points over a state-of-the-art parser and reach 93.95 UAS, which is the highest reported accuracy score on dependency parsing of the Penn Treebank. The Phrase Structure parser obtains 91.72 F-score with the features derived from the dependency trees, and this is also competitive with the best reported PARSEVAL scores for the Penn Treebank.
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COLING - Stacking of Dependency and Phrase Structure Parsers
2012Co-Authors: Richard Farkas, Bernd BohnetAbstract:We investigate the stacking of dependency and Phrase Structure parsers, i.e. we define features from the output of a Phrase Structure parser for a dependency parser and vice versa. Our features are based on the original form of the external parses and we also compare this approach to converting Phrase Structures to dependencies then applying standard stacking on the converted output. The proposed method provides high accuracy gains for both Phrase Structure and dependency parsing. With the features derived from the Phrase Structures, we achieved a gain of 0.89 percentage points over a state-of-the-art parser and reach 93.95 UAS, which is the highest reported accuracy score on dependency parsing of the Penn Treebank. The Phrase Structure parser obtains 91.72 F-score with the features derived from the dependency trees, and this is also competitive with the best reported PARSEVAL scores for the Penn Treebank.
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IWPT - Features for Phrase-Structure Reranking from Dependency Parses
2011Co-Authors: Richard Farkas, Bernd Bohnet, Helmut SchmidAbstract:Radically different approaches have been proved to be effective for Phrase-Structure and dependency parsers in the last decade. Here, we aim to exploit the divergence in these approaches and show the utility of features extracted from the automatic dependency parses of sentences for a discriminative Phrase-Structure parser. Our experiments show a significant improvement over the state-of-the-art German discriminative constituent parser.
Johan Hall - One of the best experts on this subject based on the ideXlab platform.
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parsing discontinuous Phrase Structure with grammatical functions
International conference natural language processing, 2008Co-Authors: Johan Hall, Joakim NivreAbstract:This paper presents a novel technique for parsing discontinuous Phrase Structure representations, labeled with both Phrase labels and grammatical functions. Phrase Structure representations are transformed into dependency representations with complex edge labels, which makes it possible to induce a dependency parser model that recovers the Phrase Structure with both Phrase labels and grammatical functions. We perform an evaluation on the German TIGER treebank and the Swedish Talbanken05 treebank and report competitive results for both data sets.
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GoTAL - Parsing Discontinuous Phrase Structure with Grammatical Functions
Advances in Natural Language Processing, 2008Co-Authors: Johan Hall, Joakim NivreAbstract:This paper presents a novel technique for parsing discontinuous Phrase Structure representations, labeled with both Phrase labels and grammatical functions. Phrase Structure representations are transformed into dependency representations with complex edge labels, which makes it possible to induce a dependency parser model that recovers the Phrase Structure with both Phrase labels and grammatical functions. We perform an evaluation on the German TIGER treebank and the Swedish Talbanken05 treebank and report competitive results for both data sets.
