discriminative learning
Self Discriminative Learning for Unsupervised Document Embedding
... We note that averaging subsequences differs from averaging of words in two aspects. First, each sentence is encoded individually before be- ing averaged, allowing incorporation of word or- der into design rationale at ...
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Discriminative Learning of Syntactic and Semantic Dependencies
... for discriminative learning of syntactic and semantic dependencies submitted to the CoNLL-2008 shared task (Surdeanu, et ...dependency learning task to classification issues and reconstructs the ...
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Refining Generative Language Models using Discriminative Learning
... Discriminative learning methods require negative ...on discriminative language modeling this was not a major issue as the work was concerned with specific applications, and these provided a natural ...
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Discriminative Learning for Joint Template Filling
... Interestingly, several researchers have attempted to model label consistency and high-level relational constraints using state-of-the-art sequential models of named entity recognition (NER). Mainly, pre- determined ...
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A Discriminative Learning Model for Coordinate Conjunctions
... To address these problems, we propose a new framework for detecting and disambiguating coor- dinate conjunctions. Being a discriminative learning model, it can incorporate a large number of overlap- ping ...
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Discriminative Learning of Selectional Preference from Unlabeled Text
... In particular, we would like to exploit a number of arbitrary and potentially overlapping properties of predicates and arguments when we assign SPs. We do this by representing these properties as fea- tures in a linear ...
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Object detection and segmentation using discriminative learning
... using discriminative fitting functions consistently outper- form ASM and AAM by a large margin in the three ...using discriminative boundary classifiers; however, it still falls into the local extremes ...
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Discriminative Learning with Natural Annotations: Word Segmentation as a Case Study
... Different from the dense and accurate annota- tions in human-annotated corpora, natural annota- tions in web text are sparse and slight, it makes direct training of NLP models impracticable. In this work we take for ...
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Investigating Loss Functions and Optimization Methods for Discriminative Learning of Label Sequences
... Discriminative models have been of inter- est in the NLP community in recent years. Previous research has shown that they are advantageous over generative mod- els. In this paper, we investigate how dif- ferent ...
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Discriminative Learning over Constrained Latent Representations
... Joint Learning Algorithm In contrast to most existing approaches that employ domain specific heuristics to construct intermediate representations to learn the final classifier, our algorithm learns to construct ...
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Reducing Weight Undertraining in Structured Discriminative Learning
... Discriminative probabilistic models are very popular in NLP because of the latitude they afford in designing features. But training involves complex trade-offs among weights, which can be dangerous: a few highly- ...
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Multi Document Summarization Using A* Search and Discriminative Learning
... In this paper we have proposed an A* search ap- proach for generating a summary from a ranked list of sentences and learning feature weights for a fea- ture based extractive multi-document summariza- tion system. ...
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Cross Lingual Discriminative Learning of Sequence Models with Posterior Regularization
... for learning weakly-supervised systems (in their case, dependency parsers) that incorporated alignment-based information too, but used the cross- lingual information only as soft constraints, via poste- rior ...
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“Discriminative Learning with Hybridised framework for Obtaining the Named Entity Recognition”
... Many existing Natural language processing techniques heavily rely on linguistic features, such as part of speech tags of the surrounding words, word capitalization, trigger words (e.g., Mr., Dr.), and gazetteers. These ...
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Discriminative Learning Under Covariate Shift
... purely discriminative: neither training nor test distribution are modeled ...of learning under covariate shift can be written as an integrated optimization ...
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Phrase Clustering for Discriminative Learning
... supervised learning algorithms have gained widespread acceptance in natural language processing ...supervised learning to an NLP problem, one first represents the problem as a vector of ...The ...
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Discriminative Learning of Max-Sum Classifiers
... for learning linear classifiers which has proved to be successful in numerous ap- ...allows learning also from an inconsistent training set. Learning is formulated as minimization of a regularized ...
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Confidence Weighted Learning of Factored Discriminative Language Models
... confidence-weighted learning (DLM 2) and confidence-weighted learning with soft mar- gin (DLM ...All discriminative language models strongly reduce the error rate compared to the base- line ...these ...
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PubMedCentral-PMC5708892.pdf
... the discriminative power of BN models for continuous variables from two different ...general discriminative learning frameworks for Gaussian Bayesian networks ...the discriminative classifiers ...
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Multi Task Learning for Improved Discriminative Training in SMT
... multi-task learning experiments are based on pairwise ranking perceptrons that differ in their objective, corresponding either to the orig- inal perceptron or to the ...
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