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1,372
Learning correction grammars
- Proceedings of the 20th Annual Conference on Learning Theory, 2007. To appear. 10 For u > 1, u; b 2 N, the
"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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Cited by 1 (0 self)
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All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Learnability in Optimality Theory
, 1995
"... In this article we show how Optimality Theory yields a highly general Constraint Demotion principle for grammar learning. The resulting learning procedure specifically exploits the grammatical structure of Optimality Theory, independent of the content of substantive constraints defining any given gr ..."
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Cited by 529 (35 self)
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efficient convergence to a correct grammar. We discuss implications for learning from overt data only, as well as other learning issues. We argue that Optimality Theory promotes confluence of the demands of more effective learnability and deeper linguistic explanation.
Correction Grammars for Error Handling in a Speech Dialog System
"... Speech recognition errors are inevitable in a speech dialog system. This paper presents an error handling method based on correction grammars which recognize the correction utterances which follow a recognition error. Correction grammars are dynamically created from existing grammars and a set of co ..."
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Cited by 4 (0 self)
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Speech recognition errors are inevitable in a speech dialog system. This paper presents an error handling method based on correction grammars which recognize the correction utterances which follow a recognition error. Correction grammars are dynamically created from existing grammars and a set
Recognition of visual activities and interactions by stochastic parsing
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2000
"... This paper describes a probabilistic syntactic approach to the detection and recognition of temporally extended activities and interactions between multiple agents. The fundamental idea is to divide the recognition problem into two levels. The lower level detections are performed using standard inde ..."
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Cited by 322 (8 self)
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independent probabilistic event detectors to propose candidate detections of low-level features. The outputs of these detectors provide the input stream for a stochastic context-free grammar parsing mechanism. The grammar and parser provide longer range temporal constraints, disambiguate uncertain low
Towards History-based Grammars: Using Richer Models for Probabilistic Parsing
- In Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics
, 1993
"... We describe a generarive probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity. HBG incorporates lexical, syntactic, semantic, and structural information from the parse tree into the disambiguation process in a novel ..."
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Cited by 162 (6 self)
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way. We use a corpus of bracketed sentences, called a Treebank, in combination with decision tree building to tease out the relevant aspects of a parse tree that will determine the correct parse of a sentence. This stands in contrast to the usual approach of further grammar tailoring via the usual
The case against grammar correction in L2 writing classes. Language Learning 46
, 1996
"... Ferris (1999) rejects my case against grammar correction in L2 writing classes (Truscott, 1996) and attempts to build her own case for the practice. This paper responds to her criticisms. I argue that these criticisms are both unfounded and highly selective, leaving large portions of my case unchal- ..."
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Cited by 134 (3 self)
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Ferris (1999) rejects my case against grammar correction in L2 writing classes (Truscott, 1996) and attempts to build her own case for the practice. This paper responds to her criticisms. I argue that these criticisms are both unfounded and highly selective, leaving large portions of my case unchal
Training Paradigms for Correcting Errors in Grammar and Usage
- In Proceedings of HLT-NAACL-10. Los
, 2010
"... This paper proposes a novel approach to the problem of training classifiers to detect and correct grammar and usage errors in text by selectively introducing mistakes into the training data. When training a classifier, we would like the distribution of examples seen in training to be as similar as p ..."
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Cited by 28 (9 self)
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This paper proposes a novel approach to the problem of training classifiers to detect and correct grammar and usage errors in text by selectively introducing mistakes into the training data. When training a classifier, we would like the distribution of examples seen in training to be as similar
Tuning a Grammar Correction
"... In this paper, we propose two enhance-ments to a statistical machine translation based approach to grammar correction for correcting all error categories. First, we propose tuning the SMT systems to op-timize a metric more suited to the gram-mar correction task (F-β score) rather than the traditiona ..."
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In this paper, we propose two enhance-ments to a statistical machine translation based approach to grammar correction for correcting all error categories. First, we propose tuning the SMT systems to op-timize a metric more suited to the gram-mar correction task (F-β score) rather than
Winnow Based Grammar Correction
"... I attempt to perform context-sensitive grammar correction on frequently confused pairs of words. This provides a machine learning problem with a high dimensional space where many factors are irrelevant. I implement a system similar to Winnow-2 to allow for generating a decision hyper-plane while sti ..."
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I attempt to perform context-sensitive grammar correction on frequently confused pairs of words. This provides a machine learning problem with a high dimensional space where many factors are irrelevant. I implement a system similar to Winnow-2 to allow for generating a decision hyper-plane while
Wide-coverage efficient statistical parsing with CCG and log-linear models
- COMPUTATIONAL LINGUISTICS
, 2007
"... This paper describes a number of log-linear parsing models for an automatically extracted lexicalized grammar. The models are "full" parsing models in the sense that probabilities are defined for complete parses, rather than for independent events derived by decomposing the parse tree. Dis ..."
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Cited by 218 (43 self)
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. Discriminative training is used to estimate the models, which requires incorrect parses for each sentence in the training data as well as the correct parse. The lexicalized grammar formalism used is Combinatory Categorial Grammar (CCG), and the grammar is automatically extracted from CCGbank, a CCG version
Results 1 - 10
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