Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Development of corpora within the CLaRK system: the BulTreeBank project experience
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 2
Shallow language processing architecture for Bulgarian
COLING '02 Proceedings of the 19th 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
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Incremental parsing with the perceptron algorithm
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Arabic tokenization, part-of-speech tagging and morphological disambiguation in one fell swoop
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Bidirectional inference with the easiest-first strategy for tagging sequence data
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Context-based morphological disambiguation with random fields
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Icelandic data driven part of speech tagging
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Alternating projections for learning with expectation constraints
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Cross-lingual adaptation as a baseline: adapting maximum entropy models to Bulgarian
AdaptLRTtoND '09 Proceedings of the Workshop on Adaptation of Language Resources and Technology to New Domains
A multi-domain web-based algorithm for POS tagging of unknown words
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Web-scale features for full-scale parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Semisupervised condensed nearest neighbor for part-of-speech tagging
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Learning with lookahead: can history-based models rival globally optimized models?
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Linguistically-augmented Bulgarian-to-English statistical machine translation model
EACL 2012 Proceedings of the Joint Workshop on Exploiting Synergies between Information Retrieval and Machine Translation (ESIRMT) and Hybrid Approaches to Machine Translation (HyTra)
Linguistically-enriched models for Bulgarian-to-English machine translation
SSST-6 '12 Proceedings of the Sixth Workshop on Syntax, Semantics and Structure in Statistical Translation
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We present experiments with part-of-speech tagging for Bulgarian, a Slavic language with rich inflectional and derivational morphology. Unlike most previous work, which has used a small number of grammatical categories, we work with 680 morpho-syntactic tags. We combine a large morphological lexicon with prior linguistic knowledge and guided learning from a POS-annotated corpus, achieving accuracy of 97.98%, which is a significant improvement over the state-of-the-art for Bulgarian.