A stochastic finite-state word-segmentation algorithm for Chinese
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Learning to Parse Natural Language with Maximum Entropy Models
Machine Learning - Special issue on natural language learning
Large Margin Classification Using the Perceptron Algorithm
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Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
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Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Ultraconservative online algorithms for multiclass problems
The Journal of Machine Learning Research
Generalized probabilistic LR parsing of natural language (Corpora) with unification-based grammars
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Probabilistic top-down parsing and language modeling
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COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Is it harder to parse Chinese, or the Chinese Treebank?
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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On the parameter space of generative lexicalized statistical parsing models
Two statistical parsing models applied to the Chinese Treebank
CLPW '00 Proceedings of the second workshop on Chinese language processing: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 12
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
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SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
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Online large-margin training of dependency parsers
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Non-projective dependency parsing using spanning tree algorithms
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CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Projective dependency parsing with perceptron
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Labeled pseudo-projective dependency parsing with support vector machines
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
TAG, dynamic programming, and the perceptron for efficient, feature-rich parsing
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Word lattice reranking for Chinese word segmentation and part-of-speech tagging
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
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EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Concise integer linear programming formulations for dependency parsing
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
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ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
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IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
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EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Dynamic programming for linear-time incremental parsing
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Parsing the penn chinese treebank with semantic knowledge
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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ACM Transactions on Asian Language Information Processing (TALIP)
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EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
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EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
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IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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We study a range of syntactic processing tasks using a general statistical framework that consists of a global linear model, trained by the generalized perceptron together with a generic beam-search decoder. We apply the framework to word segmentation, joint segmentation and POS-tagging, dependency parsing, and phrase-structure parsing. Both components of the framework are conceptually and computationally very simple. The beam-search decoder only requires the syntactic processing task to be broken into a sequence of decisions, such that, at each stage in the process, the decoder is able to consider the top-n candidates and generate all possibilities for the next stage. Once the decoder has been defined, it is applied to the training data, using trivial updates according to the generalized perceptron to induce a model. This simple framework performs surprisingly well, giving accuracy results competitive with the state-of-the-art on all the tasks we consider. The computational simplicity of the decoder and training algorithm leads to significantly higher test speeds and lower training times than their main alternatives, including log-linear and large-margin training algorithms and dynamic-programming for decoding. Moreover, the framework offers the freedom to define arbitrary features which can make alternative training and decoding algorithms prohibitively slow. We discuss how the general framework is applied to each of the problems studied in this article, making comparisons with alternative learning and decoding algorithms. We also show how the comparability of candidates considered by the beam is an important factor in the performance. We argue that the conceptual and computational simplicity of the framework, together with its language-independent nature, make it a competitive choice for a range of syntactic processing tasks and one that should be considered for comparison by developers of alternative approaches.