Scaling question answering to the Web
Proceedings of the 10th international conference on World Wide Web
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Combining distributional and morphological information for part of speech induction
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
A syntax-based statistical translation model
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
A generative constituent-context model for improved grammar induction
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
In question answering, two heads are better than one
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
COGEX: a logic prover for question answering
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
The unsupervised learning of natural language structure
The unsupervised learning of natural language structure
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Corpus-based induction of syntactic structure: models of dependency and constituency
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Annealing structural bias in multilingual weighted grammar induction
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
An all-subtrees approach to unsupervised parsing
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Prototype-driven grammar induction
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Word-Level Confidence Estimation for Machine Translation
Computational Linguistics
The importance of syntactic parsing and inference in semantic role labeling
Computational Linguistics
Self-training for biomedical parsing
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Computing confidence scores for all sub parse trees
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Unsupervised parsing with U-DOP
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Learning reliable information for dependency parsing adaptation
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Unsupervised induction of labeled parse trees by clustering with syntactic features
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Detecting parser errors using web-based semantic filters
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Automatic prediction of parser accuracy
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Confidence estimation for information extraction
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Improved fully unsupervised parsing with zoomed learning
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Posterior Sparsity in Unsupervised Dependency Parsing
The Journal of Machine Learning Research
ULISSE: an unsupervised algorithm for detecting reliable dependency parses
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
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The average results obtained by unsupervised statistical parsers have greatly improved in the last few years, but on many specific sentences they are of rather low quality. The output of such parsers is becoming valuable for various applications, and it is radically less expensive to create than manually annotated training data. Hence, automatic selection of high quality parses created by unsupervised parsers is an important problem. In this paper we present PUPA, a POS-based Unsupervised Parse Assessment algorithm. The algorithm assesses the quality of a parse tree using POS sequence statistics collected from a batch of parsed sentences. We evaluate the algorithm by using an unsupervised POS tagger and an unsupervised parser, selecting high quality parsed sentences from English (WSJ) and German (NEGRA) corpora. We show that PUPA outperforms the leading previous parse assessment algorithm for supervised parsers, as well as a strong unsupervised baseline. Consequently, PUPA allows obtaining high quality parses without any human involvement.