[PDF] Top 20 Structured Penalties for Log Linear Language Models
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Structured Penalties for Log Linear Language Models
... Language models are crucial parts of advanced nat- ural language processing pipelines, such as speech recognition (Burget et ...a language model pre- dicts the probability of occurrence of the ... See full document
11
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 ... See full document
8
Perceptron Reranking for CCG Realization
... including language model log probabilities as features in the model, which prior work on discriminative training with log linear models for HPSG realization had called into ...n-gram ... See full document
10
A Virtual Manipulative for Learning Log Linear Models
... Log-linear models can be also used for struc- tured prediction problems in NLP such as tagging, parsing, chunking, segmentation, and language ...conditional log-linear model ... See full document
11
Computationally Efficient M Estimation of Log Linear Structure Models
... Log-linear models are a very popular tool in natural language processing, and are often lauded for per- mitting the use of “arbitrary” and “correlated” fea- tures of the data by a ...of ... See full document
8
Unsupervised Morphological Segmentation with Log Linear Models
... natural language pro- cessing ...ery language there are virtually unlimited sup- plies of text, but very few labeled ...generative models, making it dif- ficult to leverage arbitrary overlapping fea- ... See full document
9
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 ... See full document
6
A principled approach to the implementation of argumentation models
... contrast, structured argumentation not based on logic programming still lacks a completely satisfying programming ...suitable language for abstract as well as structured models by implementing ... See full document
8
Log Linear Models for Word Alignment
... on log-linear ...target language sentence and possible additional vari- ables. Log-linear models allow statis- tical alignment models to be easily ex- tended by ... See full document
8
TriS: A Statistical Sentence Simplifier with Log linear Models and Margin based Discriminative Training
... natural language processing applications including, but not limited to, text summarization, question answering, information extraction, and machine translation (Chandrasekar et ... See full document
9
Training Connectionist Models for the Structured Language Model
... the Structured Language Model (SLM) in terms of perplexity (PPL) when its compo- nents are modeled by connectionist mod- ...connectionist models use a dis- tributed representation of the items in the ... See full document
8
Using Log-linear Models for Tuning Machine Translation Output
... n-gram language models (LMs) from tokenized ...closed language model since there are less than 70 different tags in this tag set and all tags are likely to occur in the training ... See full document
8
Contrastive Estimation: Training Log Linear Models on Unlabeled Data
... Natural language is a delicate thing. For any plausi- ble sentence, there are many slight perturbations of it that will make it implausible. Consider, for ex- ample, the first sentence of this section. Suppose we ... See full document
9
Log-linear Models for Uyghur Segmentation in Spoken Language Translation
... formation, it can’t alleviate the data sparsity effec- tively in spoken Uyghur-Chinese machine transla- tion. Therefore, stem-based models both outper- form factored based translation model in test set. Our ... See full document
9
Normalized Log Linear Interpolation of Backoff Language Models is Efficient
... that log-linearly interpolated backoff language models can be efficiently and exactly collapsed into a single nor- malized backoff model, contradicting Hsu ...that log-linear ... See full document
11
Structured penalties for functional linear models—partially empirical eigenvectors for regression
... of structured penalties including two previously-proposed special cases that were justified by numerical ...targeted penalties of subsection ... See full document
31
Feature Noising for Log Linear Structured Prediction
... in log-linear models where second derivatives are ...sequence models that are ubiquitous in NLP, via linear chain Conditional Ran- dom Fields ... See full document
10
Unsupervised morph segmentation and statistical language models for vocabulary expansion
... limited language pack ...guage models provided to our understanding the best results on the task so ...statistical language models were used directly in the word generation ... See full document
6
Analyzing The Suitability Of The Spline Models And Other Forecasting Models In The Estimation Of Cassava Production In Nigeria.
... polynomial models (spline with knots, spline without knots), linear, semi-log and growth models is evaluated using historical trace path of the observed data, level of the significant ... See full document
9
Causal Effect Estimation Under Linear and Log-Linear Structural Nested Mean Models in the Presence of Unmeasured Confounding
... estimating linear direct effects of the randomized interven- tion on outcome without any assumptions regarding unmeasured confounding of the mediator-outcome ...the linear SNMM approach under no assumptions ... See full document
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