Tagging text with a probabilistic model

Mérialdo, Bernard

doi:10.1109/icassp.1991.150460

Cited by 192 publications

(262 citation statements)

References 12 publications

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“…We used a smoothing factor of 0.125. See [10] for the application of smoothing in a similar problem. Other methods like Laplace's rule may be used instead [1].…”

Section: Modifying Production Probabilitiesmentioning

confidence: 99%

Towards Heuristic Algorithmic Memory

Özkural

2011

Artificial General Intelligence

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Abstract. We propose a long-term memory design for artificial general intelligence based on Solomonoff's incremental machine learning methods. We introduce four synergistic update algorithms that use a Stochastic Context-Free Grammar as a guiding probability distribution of programs. The update algorithms accomplish adjusting production probabilities, re-using previous solutions, learning programming idioms and discovery of frequent subprograms. A controlled experiment with a long training sequence shows that our incremental learning approach is effective.

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“…We used a smoothing factor of 0.125. See [10] for the application of smoothing in a similar problem. Other methods like Laplace's rule may be used instead [1].…”

Section: Modifying Production Probabilitiesmentioning

confidence: 99%

Towards Heuristic Algorithmic Memory

Özkural

2011

Artificial General Intelligence

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“…This is the case of the Xerox tagger (Cutting et al, 1992) and its successors. Those interested in the subject can find an excellent overview by Merialdo (1994).…”

Section: Existing Approaches To Pos Taggingmentioning

confidence: 99%

“…In POS tagging we find the Baum-Welch re-estimation algorithm which has been successfully used to improve tagger accuracies when limited disambiguated material is available (Cutting et al, 1992;Elworthy, 1994;Merialdo, 1994). Brill (1995) presented a weak-supervised version of the transformation-based learning algorithm for tagging.…”

Section: Tagging the Lexesp Spanish Corpusmentioning

confidence: 99%

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2000

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Abstract.We have applied the inductive learning of statistical decision trees and relaxation labeling to the Natural Language Processing (NLP) task of morphosyntactic disambiguation (Part Of Speech Tagging). The learning process is supervised and obtains a language model oriented to resolve POS ambiguities, consisting of a set of statistical decision trees expressing distribution of tags and words in some relevant contexts. The acquired decision trees have been directly used in a tagger that is both relatively simple and fast, and which has been tested and evaluated on the Wall Street Journal (WSJ) corpus with competitive accuracy. However, better results can be obtained by translating the trees into rules to feed a flexible relaxation labeling based tagger. In this direction we describe a tagger which is able to use information of any kind (n-grams, automatically acquired constraints, linguistically motivated manually written constraints, etc.), and in particular to incorporate the machine-learned decision trees. Simultaneously, we address the problem of tagging when only limited training material is available, which is crucial in any process of constructing, from scratch, an annotated corpus. We show that high levels of accuracy can be achieved with our system in this situation, and report some results obtained when using it to develop a 5.5 million words Spanish corpus from scratch.

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“…Many proposed solutions are based on Hidden Markov models (HMMs), with various improvements obtainable through: inductive bias in the form of tag dictionaries (Kupiec, 1992;Merialdo, 1994), sparsity constraints (Lee et al, 2010), careful initialization of parameters (Goldberg et al, 2008), feature based representations (Berg-Kirkpatrick et al, 2010;Smith and Eisner, 2005), and priors on model parameters (Johnson, 2007;Goldwater and Griffiths, 2007;Blunsom and Cohn, 2011, inter alia).…”

Section: Introductionmentioning

confidence: 99%

Weakly-Supervised Bayesian Learning of a CCG Supertagger

Garrette¹,

Dyer²,

Baldridge³

et al. 2014

Proceedings of the Eighteenth Conference on Computational Natural Language Learning

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We present a Bayesian formulation for weakly-supervised learning of a Combinatory Categorial Grammar (CCG) supertagger with an HMM. We assume supervision in the form of a tag dictionary, and our prior encourages the use of crosslinguistically common category structures as well as transitions between tags that can combine locally according to CCG's combinators. Our prior is theoretically appealing since it is motivated by languageindependent, universal properties of the CCG formalism. Empirically, we show that it yields substantial improvements over previous work that used similar biases to initialize an EM-based learner. Additional gains are obtained by further shaping the prior with corpus-specific information that is extracted automatically from raw text and a tag dictionary.

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Tagging text with a probabilistic model

Cited by 192 publications

References 12 publications

Towards Heuristic Algorithmic Memory

Towards Heuristic Algorithmic Memory

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Weakly-Supervised Bayesian Learning of a CCG Supertagger

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