Log linear models
Log Linear Models for Word Alignment
... (3) Typically, the source language sentence e and the target sentence f are the fundamental knowledge sources for the task of finding word alignments. Lin- guistic data, which can be used to identify associ- ations ...
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Wide Coverage Efficient Statistical Parsing with CCG and Log Linear Models
... The main differences between Miyao and Tsujii’s work and ours, aside from the different grammar formalisms, are as follows. The CCG supertagger is a key component of our parsing system. It allows practical estimation of ...
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Minimum Risk Annealing for Training Log Linear Models
... with log- linear ...sider log-linear combinations of a relatively small number of features over entire complex structures, such as trees or translations, known in some pre- vious work as ...
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Power of edge exclusion tests for graphical log linear models
... Graphical log-linear models are a subclass of hierarchical log-linear models (see, for example, Agresti [1, ...GLL models can be interpreted solely in terms of con- ...
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Contrastive Estimation: Training Log Linear Models on Unlabeled Data
... of log- linear models—specifically, their ability to incorpo- rate novel features—we also ran trials augmenting the model with spelling features, allowing exploita- tion of correlations between parts ...
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Assessing identification risk in survey microdata using log linear models
... Poisson log-linear models to estimate disclosure risk mea- sures for microdata, with applications to census and survey ...select models that show appreciable improvements in risk estimation ...
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Parameter redundancy and the existence of maximum likelihood estimates in log linear models
... Abstract: Log-linear models are typically fitted to contingency table data to de- scribe and identify the relationship between different categorical ...given log-linear model is ...
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Log Linear Models for Wide Coverage CCG Parsing
... handle. Log-linear models have pre- viously been applied to statistical pars- ing, under the assumption that all possible parses for a sentence can be ...
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Results on point and interval estimation for log linear models with non ignorable non response
... that log-linear models for multi-way contingency tables with one variable subject to non- ignorable non-response will yield non-response boundary solutions, where the probability of non- respondents ...
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TriS: A Statistical Sentence Simplifier with Log linear Models and Margin based Discriminative Training
... We propose a statistical sentence simplifica- tion system with log-linear models. In contrast to state-of-the-art methods that drive sentence simplification process by hand-written linguis- tic ...
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Stochastic Gradient Descent Training for L1 regularized Log linear Models with Cumulative Penalty
... compact models, cannot be effi- ciently applied in SGD training, due to the large dimensions of feature vectors and the fluctuations of approximate gra- ...accurate models much more quickly than a ...
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Correction of Verication Bias using Log-Linear Models for a Single Binaryscale Diagnostic Tests
... the bias and improves the performance of the estimators compared to complete case analysis. In addition to this, explicit estimates for the measures of diagnostic accuracy under different missing mechanisms can be ...
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“Love ya, jerkface”: Using Sparse Log Linear Models to Build Positive and Impolite Relationships with Teens
... of log-linear models, we are able to investigate the contributions of individual lan- guage behaviors in one student’s turn to the predic- tion of social functions in their partner’s next ...
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A Virtual Manipulative for Learning Log Linear Models
... (unregularized) log-likelihood. Thus the mismatch is 0 iff the gradient of log- likelihood is ...the log-likelihood ...of log-likelihood can easily be computed by summing up the observed and ...
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When and why are log linear models self normalizing?
... in log-linear approaches to language ...conventional log-linear models (Rosen- feld, 1994; Biadsy et ...a log-linear output layer (Bengio et ...generative models ...
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Unsupervised Morphological Segmentation with Log Linear Models
... with log-linear models has received little attention in the ...with log-linear models requires computing the normalization constant ...
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Log Linear Models for Religious and Social Factors affecting the practice of Family Planning Methods in Lahore, Pakistan
... Hakim (1995) examined that use of family planning methods in Pakistan is determined by various factors and may vary between difference segments if population according to various socio-economic, cultural and economic ...
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Weighted log-linear models for service delivery points in Ethiopia: a case of modern contraceptive users at health facilities
... weighted log-linear model was used that proposed by Agresti ...weighted log-linear model link function can be fitted as: log ð μ ...
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Log Linear Models of Non Projective Trees, k best MST Parsing and Tree Ranking
... Edge-factored models are severely limited in their capacity to predict structure. In fact, they can only directly model parent-child links. In order to allevi- ate this, we use a k-best MST parser to generate a ...
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Using Log-linear Models for Tuning Machine Translation Output
... Weights were between 0 and 1.0 and the best results shown in the Figures 1 to 4 use an equal weight of 0.6 for the token, lemma and tag models. Then we added further feature functions to this baseline scenario . ...
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