Deducing linguistic structure from the statistics of large corpora
HLT '90 Proceedings of the workshop on Speech and Natural Language
Natural language parsing as statistical pattern recognition
Natural language parsing as statistical pattern recognition
Chinese text retrieval without using a dictionary
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Learning to Parse Natural Language with Maximum Entropy Models
Machine Learning - Special issue on natural language learning
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Partial parsing via finite-state cascades
Natural Language Engineering
Three generative, lexicalised models for statistical parsing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Statistical decision-tree models for parsing
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
A maximum-entropy chinese parser augmented by transformation-based learning
ACM Transactions on Asian Language Information Processing (TALIP)
The Penn Chinese TreeBank: Phrase structure annotation of a large corpus
Natural Language Engineering
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Parsing, word associations and typical predicate-argument relations
HLT '89 Proceedings of the workshop on Speech and Natural Language
On the parameter space of generative lexicalized statistical parsing models
On the parameter space of generative lexicalized statistical parsing models
Head-Driven Statistical Models for Natural Language Parsing
Computational Linguistics
Using co-occurrence statistics as an information source for partial parsing of Chinese
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
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Deterministic dependency parsing of English text
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
A best-first probabilistic shift-reduce parser
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Dependency Parsing
A classifier-based parser with linear run-time complexity
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Parsing a natural language using mutual information statistics
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
A text-based decision support system for financial sequence prediction
Decision Support Systems
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Despite the popularity of lexicalized parsing models, practical concerns such as data sparseness and applicability to domains of different vocabularies make unlexicalized models that do not refer to word tokens themselves deserve more attention. A classifier-based parser using an unlexicalized parsing model has been developed. Most importantly, to enhance the accuracy of these tasks, we investigated the notion of distituency (the possibility that two parts of speech cannot remain in the same constituent or phrase) and incorporated it as attributes using various statistic measures. A machine learning method integrates linguistic attributes and information-theoretic attributes in two tasks, namely sentence chunking and phrase recognition. The parser was applied to parsing English and Chinese sentences in the Penn Treebank and the Tsinghua Chinese Treebank. It achieved a parsing performance of F-Score 80.3% in English and 82.4% in Chinese.