Grammatical category disambiguation by statistical optimization
Computational Linguistics
Probabilistic models of short and long distance word dependencies in running text
HLT '89 Proceedings of the workshop on Speech and Natural Language
Hidden Markov Models for Speech Recognition
Hidden Markov Models for Speech Recognition
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Tagging English text with a probabilistic model
Computational Linguistics
A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
A practical part-of-speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
A Review of Statistical Language Processing Techniques
Artificial Intelligence Review
A Machine Learning Approach to POS Tagging
Machine Learning
Cohesive Generation of Syntactically Simplified Newspaper Text
TDS '00 Proceedings of the Third International Workshop on Text, Speech and Dialogue
Exploitation of Unlabeled Sequences in Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic extraction of subcategorization from corpora
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
A syntax-based part-of-speech analyser
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
Parsing with an extended domain of locality
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Minimizing manual annotation cost in supervised training from corpora
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Improving part-of-speech tagging using lexicalized HMMs
Natural Language Engineering
A comparison of parsing technologies for the biomedical domain
Natural Language Engineering
Detecting novel compounds: the role of distributional evidence
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Using grammatical relations to compare parsers
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
XML-based data preparation for robust deep parsing
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Applying co-training methods to statistical parsing
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Improving subcategorization acquisition using word sense disambiguation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Disambiguating Nouns, Verbs, and Adjectives Using Automatically Acquired Selectional Preferences
Computational Linguistics
XML-based NLP tools for analysing and annotating medical language
NLPXML '02 Proceedings of the 2nd workshop on NLP and XML - Volume 17
Bootstrapping POS taggers using unlabelled data
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Definitional, personal, and mechanical constraints on part of speech annotation performance
Natural Language Engineering
The importance of the lexicon in tagging biological text
Natural Language Engineering
Annealing techniques for unsupervised statistical language learning
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
An unsupervised morpheme-based HMM for hebrew morphological disambiguation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Toward unsupervised whole-corpus tagging
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Part of speech tagging in context
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
The second release of the RASP system
COLING-ACL '06 Proceedings of the COLING/ACL on Interactive presentation sessions
Artificial Intelligence in Medicine
Part-of-speech tagging of modern hebrew text
Natural Language Engineering
A Fault Prediction Model with Limited Fault Data to Improve Test Process
PROFES '08 Proceedings of the 9th international conference on Product-Focused Software Process Improvement
Towards full automation of lexicon construction
CLS '04 Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics
Semi-supervised training of a statistical parser from unlabeled partially-bracketed data
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Refining the most frequent sense baseline
DEW '09 Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions
Painless unsupervised learning with features
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
From baby steps to Leapfrog: how "Less is More" in unsupervised dependency parsing
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
HMMs, GRs, and n-grams as lexical substitution techniques: are they portable to other languages?
MCTLLL '09 Proceedings of the Workshop on Natural Language Processing Methods and Corpora in Translation, Lexicography, and Language Learning
Viterbi training improves unsupervised dependency parsing
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
A comparison of unsupervised methods for part-of-speech tagging in Chinese
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Unsupervised structure prediction with non-parallel multilingual guidance
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Lateen EM: unsupervised training with multiple objectives, applied to dependency grammar induction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Adaptive Bayesian HMM for Fully Unsupervised Chinese Part-of-Speech Induction
ACM Transactions on Asian Language Information Processing (TALIP)
Disambiguating noun and verb senses using automatically acquired selectional preferences
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
A ruled-based part of speech (RPOS) tagger for malay text articles
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
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In part of speech tagging by Hidden Markov Model, a statistical model is used to assign grammatical categories to words in a text. Early work in the field relied on a corpus which had been tagged by a human annotator to train the model. More recently, Cutting et al. (1992) suggest that training can be achieved with a minimal lexicon and a limited amount of a priori information about probabilities, by using an Baum-Welch re-estimation to automatically refine the model. In this paper, I report two experiments designed to determine how much manual training information is needed. The first experiment suggests that initial biasing of either lexical or transition probabilities is essential to achieve a good accuracy. The second experiment reveals that there are three distinct patterns of Baum-Welch reestimation. In two of the patterns, the re-estimation ultimately reduces the accuracy of the tagging rather than improving it. The pattern which is applicable can be predicted from the quality of the initial model and the similarity between the tagged training corpus (if any) and the corpus to be tagged. Heuristics for deciding how to use re-estimation in an effective manner are given. The conclusions are broadly in agreement with those of Merialdo (1994), but give greater detail about the contributions of different parts of the model.