Grammatical category disambiguation by statistical optimization
Computational Linguistics
A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Constraint Grammar: A Language-Independent System for Parsing Unrestricted Text
Constraint Grammar: A Language-Independent System for Parsing Unrestricted Text
A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
Tagging accurately: don't guess if you know
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
Tagging and morphological disambiguation of Turkish text
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
A practical part-of-speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Tagging French: comparing a statistical and a constraint-based method
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
A syntax-based part-of-speech analyser
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
Specifying a shallow grammatical representation for parsing purposes
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
Acquiring disambiguation rules from text
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
Constraint grammar as a framework for parsing running text
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
Part-of-speech tagging with neural networks
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Handling sparse data by successive abstraction
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
A Machine Learning Approach to POS Tagging
Machine Learning
A closer look at the automatic induction of linguistic knowledge
Learning language in logic
Review of "Optimality theory" by René Kager. Cambridge University Press 1999.
Computational Linguistics
A freely available morphological analyzer, disambiguator and context sensitive lemmatizer for German
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
On the evaluation and comparison of taggers: the effect of noise in testing corpora
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Japanese morphological analyzer using word co-occurrence: JTAG
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Serial combination of rules and statistics: a case study in Czech tagging
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
A low-complexity, broad-coverage probabilistic dependency parser for English
NAACLstudent '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Proceedings of the HLT-NAACL 2003 student research workshop - Volume 3
Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
High-performance tagging on medical texts
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Morphological richness offsets resource demand- experiences in constructing a POS tagger for Hindi
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
The best of two worlds: cooperation of statistical and rule-based taggers for Czech
ACL '07 Proceedings of the Workshop on Balto-Slavonic Natural Language Processing: Information Extraction and Enabling Technologies
Improving the identification of non-anaphoric it using support vector machines
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Linguistic theory in statistical language learning
NeMLaP3/CoNLL '98 Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning
Does tagging help parsing?: a case study on finite state parsing
FSMNLP '09 Proceedings of the International Workshop on Finite State Methods in Natural Language Processing
Tagging Icelandic text using a linguistic and a statistical tagger
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Part-of-speech tagging from 97% to 100%: is it time for some linguistics?
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
Improving arabic part-of-speech tagging through morphological analysis
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
On the road to high-quality POS-tagging
KI'05 Proceedings of the 28th annual German conference on Advances in Artificial Intelligence
Tagging a morphologically complex language using heuristics
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
Constraint grammar parsing with left and right sequential finite transducers
FSMNLP '11 Proceedings of the 9th International Workshop on Finite State Methods and Natural Language Processing
The DeLiVerMATH project: text analysis in mathematics
CICM'13 Proceedings of the 2013 international conference on Intelligent Computer Mathematics
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Concerning different approaches to automatic PoS tagging: EngCG-2, a constraint-based morphological tagger, is compared in a double-blind test with a state-of-the-art statistical tagger on a common disambiguation task using a common tag set. The experiments show that for the same amount of remaining ambiguity, the error rate of the statistical tagger is one order of magnitude greater than that of the rule-based one. The two related issues of priming effects compromising the results and disagreement between human annotators are also addressed.