scholarly journals Cross-lingual Projected Expectation Regularization for Weakly Supervised Learning

2014 ◽  
Vol 2 ◽  
pp. 55-66 ◽  
Author(s):  
Mengqiu Wang ◽  
Christopher D. Manning
Keyword(s):  
Supervised Learning ◽  
Model Uncertainty ◽  
State Of The Art ◽  
New Method ◽  
Weakly Supervised Learning ◽  
Supervised Methods ◽  
Weakly Supervised ◽  
Language Boundaries ◽  
Standard Chinese ◽  
Cross Lingual

We consider a multilingual weakly supervised learning scenario where knowledge from annotated corpora in a resource-rich language is transferred via bitext to guide the learning in other languages. Past approaches project labels across bitext and use them as features or gold labels for training. We propose a new method that projects model expectations rather than labels, which facilities transfer of model uncertainty across language boundaries. We encode expectations as constraints and train a discriminative CRF model using Generalized Expectation Criteria (Mann and McCallum, 2010). Evaluated on standard Chinese-English and German-English NER datasets, our method demonstrates F1 scores of 64% and 60% when no labeled data is used. Attaining the same accuracy with supervised CRFs requires 12k and 1.5k labeled sentences. Furthermore, when combined with labeled examples, our method yields significant improvements over state-of-the-art supervised methods, achieving best reported numbers to date on Chinese OntoNotes and German CoNLL-03 datasets.

scholarly journals Non-I.I.D. Multi-Instance Learning for Predicting Instance and Bag Labels with Variational Auto-Encoder

2021 ◽  
Author(s):  
Weijia Zhang
Keyword(s):  
Medical Imaging ◽  
Supervised Learning ◽  
Real World ◽  
State Of The Art ◽  
Experimental Results ◽  
Weakly Supervised Learning ◽  
Variational Autoencoder ◽  
Label Prediction ◽  
Weakly Supervised ◽  
Better Than

Multi-instance learning is a type of weakly supervised learning. It deals with tasks where the data is a set of bags and each bag is a set of instances. Only the bag labels are observed whereas the labels for the instances are unknown. An important advantage of multi-instance learning is that by representing objects as a bag of instances, it is able to preserve the inherent dependencies among parts of the objects. Unfortunately, most existing algorithms assume all instances to be identically and independently distributed, which violates real-world scenarios since the instances within a bag are rarely independent. In this work, we propose the Multi-Instance Variational Autoencoder (MIVAE) algorithm which explicitly models the dependencies among the instances for predicting both bag labels and instance labels. Experimental results on several multi-instance benchmarks and end-to-end medical imaging datasets demonstrate that MIVAE performs better than state-of-the-art algorithms for both instance label and bag label prediction tasks.

scholarly journals Explicit Document Modeling through Weighted Multiple-Instance Learning

2017 ◽  
Vol 58 ◽  
pp. 591-626 ◽  
Author(s):  
Nikolaos Pappas ◽  
Andrei Popescu-Belis
Keyword(s):  
Sentiment Analysis ◽  
Supervised Learning ◽  
State Of The Art ◽  
Multiple Instance Learning ◽  
Weakly Supervised Learning ◽  
Fixed Length ◽  
Document Modeling ◽  
Crucial Component ◽  
Document Vector ◽  
Weakly Supervised

Representing documents is a crucial component in many NLP tasks, for instance predicting aspect ratings in reviews. Previous methods for this task treat documents globally, and do not acknowledge that target categories are often assigned by their authors with generally no indication of the specific sentences that motivate them. To address this issue, we adopt a weakly supervised learning model, which jointly learns to focus on relevant parts of a document according to the context along with a classifier for the target categories. Derived from the weighted multiple-instance regression (MIR) framework, the model learns decomposable document vectors for each individual category and thus overcomes the representational bottleneck in previous methods due to a fixed-length document vector. During prediction, the estimated relevance or saliency weights explicitly capture the contribution of each sentence to the predicted rating, thus offering an explanation of the rating. Our model achieves state-of-the-art performance on multi-aspect sentiment analysis, improving over several baselines. Moreover, the predicted saliency weights are close to human estimates obtained by crowdsourcing, and increase the performance of lexical and topical features for review segmentation and summarization.

scholarly journals PsyNet: Self-Supervised Approach to Object Localization Using Point Symmetric Transformation

2020 ◽  
Vol 34 (07) ◽  
pp. 10451-10459
Author(s):  
Kyungjune Baek ◽  
Minhyun Lee ◽  
Hyunjung Shim
Keyword(s):  
Supervised Learning ◽  
State Of The Art ◽  
Object Localization ◽  
Geometric Transformation ◽  
Heat Map ◽  
Fine Grained ◽  
Current State ◽  
Supervised Methods ◽  
Weakly Supervised

Existing co-localization techniques significantly lose performance over weakly or fully supervised methods in accuracy and inference time. In this paper, we overcome common drawbacks of co-localization techniques by utilizing self-supervised learning approach. The major technical contributions of the proposed method are two-fold. 1) We devise a new geometric transformation, namely point symmetric transformation and utilize its parameters as an artificial label for self-supervised learning. This new transformation can also play the role of region-drop based regularization. 2) We suggest a heat map extraction method for computing the heat map from the network trained by self-supervision, namely class-agnostic activation mapping. It is done by computing the spatial attention map. Based on extensive evaluations, we observe that the proposed method records new state-of-the-art performance in three fine-grained datasets for unsupervised object localization. Moreover, we show that the idea of the proposed method can be adopted in a modified manner to solve the weakly supervised object localization task. As a result, we outperform the current state-of-the-art technique in weakly supervised object localization by a significant gap.

scholarly journals Weakly Supervised Learning Creates a Fusion of Modeling Cultures

2021 ◽  
Vol 7 (1) ◽  
pp. 203-211
Author(s):  
Chengliang Tang ◽  
Gan Yuan ◽  
Tian Zheng
Keyword(s):  
Supervised Learning ◽  
Weakly Supervised Learning ◽  
Weakly Supervised

Weakly Supervised Learning through Rank-based Contextual Measures

2021 ◽  
Author(s):  
Joao Gabriel Camacho Presotto ◽  
Lucas Pascotti Valem ◽  
Nikolas Gomes de Sa ◽  
Daniel Carlos Guimaraes Pedronette ◽  
Joao Paulo Papa
Keyword(s):  
Supervised Learning ◽  
Weakly Supervised Learning ◽  
Weakly Supervised
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