[PDF] Top 20 Cross Language Text Classification by Model Translation and Semi Supervised Learning
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Cross Language Text Classification by Model Translation and Semi Supervised Learning
... To alleviate this problem, we employ semi- supervised learning in order to adapt the model to the target language. Specifically, we first start by us- ing the translated classifier from ... See full document
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Cross Language Text Classification Using Structural Correspondence Learning
... that, cross-language text classification can be cast as an unsupervised domain adapta- tion problem by considering each language as a separate ...structural learning paradigm ... See full document
10
Surrogate Learning From Feature Independence to Semi Supervised Classification
... Sentence classification is often a preprocessing step for event or relation extraction from ...sentence classification is the di- versity in the language for expressing the same event or ...surrogate ... See full document
9
Semi Supervised Learning for Neural Machine Translation
... a semi- supervised approach for training NMT models on the concatenation of labeled (parallel corpora) and unlabeled (mono- lingual corpora) ...target-to-source translation models serve as the ... See full document
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Semi Supervised Learning with Auxiliary Evaluation Component for Large Scale e Commerce Text Classification
... we significantly lacked high-quality labeled data for these problems. For example, the e-commerce product categorization dataset contains product ti- tles and 600 different categories for the product titles. This dataset ... See full document
9
A Semi Supervised Bayesian Network Model for Microblog Topic Classification
... and text to represent short ...the supervised methods mainly depend on a large scale of labeled training data, which is laborious and time ...utilize semi-supervised learning meth- ods. ... See full document
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A Cross Sentence Latent Variable Model for Semi Supervised Text Sequence Matching
... Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan ... See full document
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Semi Supervised Neural Text Generation by Joint Learning of Natural Language Generation and Natural Language Understanding Models
... Results of the experiment on the E2E challenge data are summarized Table 1 for both the NLG and the NLU tasks. BLEU, Rouge-L and Meteor were computed using the E2E challenge metrics script 2 with default settings. NLU ... See full document
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LCCT: A Semi supervised Model for Sentiment Classification
... LCCT Model (Lexicon-based and Corpus-based, Co-Training Model), which treats the lexicon-based information and the corpus-based information as two views, and combine them via co-training (Blum and Mitchell, ... See full document
10
On Semi Supervised Learning of Gaussian Mixture Models for Phonetic Classification
... the semi-supervised training objective for Gaussian Mixture Models (GMM), resulting in a hybrid dis- criminative/generative objective ...phonetic classification showed significant improvement in ... See full document
9
Variational Pretraining for Semi supervised Text Classification
... to semi-supervised learning has long been a goal for the NLP community, as unlabeled data tends to be plentiful compared to labeled ...a language modeling (LM) objective on massive amounts of ... See full document
15
Cross-lingual sentiment classification using semi-supervised learning
... one language (always English) to another language with few sentiment resources and then used machine learning approach for sentiment classification, based on the projected ...machine ... See full document
53
Semi Supervised Representation Learning for Cross Lingual Text Classification
... source language do- main, even the simple cross-lingual adaptation ap- proach, CL-Dict, produces effective improvements over ...effective cross-lingual representations, espe- cially when two ... See full document
11
Learning a Deep Hybrid Model for Semi Supervised Text Classification
... ist learning stem from the expressive power af- forded by models, such as the Deep Belief Net- work (DBN) (Hinton et ...of language-based problems, deep architec- tures have outperformed popular shallow ... See full document
11
Adaptive Semi supervised Learning for Cross domain Sentiment Classification
... their model, a sub- network is added as a domain discriminator while deep features are learned to confuse the discrim- ...our model shares similar intuition with MMD and ad- versary ... See full document
10
A Review on Semi Supervised Text Classification
... in semi-supervised learning assumes that there are two classes, and in each class there is a Gaussian ...Mixture model. This particular model has been successfully applied to ... See full document
10
Heterogeneous Graph Attention Networks for Semi supervised Short Text Classification
... based model PTE achieves inferior performance compared to CNN-pretrain and ...learns text embed- dings based on word co-occurrences, which, how- ever, are sparse in short text ...Our model ... See full document
10
Multi modal curriculum learning for semi supervised image classification
... Lastly, we visualize the curriculum images selected by our MMCL during the entire teaching and learning process. When the number of labeled images is 60, our MMCL takes totally 14 propagations to classify all the ... See full document
14
A Semi Supervised Approach for Catchphrase Classification in Legal Text Documents
... the text, which can be removed without loss of relevant information for output and this, is done through the ...of text, and more than 30% of the ... See full document
11
A Survey on Data Stream and Its Various Techniques
... performs classification and novel class location in data streams while obliging little measure of labeled data for ...stream classification issues: infinite length, concept-drift, concept evolution, and ... See full document
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