Graph Based Semi-supervised Learning with Convolution Neural Networks to Classify Crisis Related Tweets

During time-critical situations such as natural disasters, rapid classification of data posted on social networks by affected people is useful for humanitarian organizations to gain situational awareness and to plan response efforts. However, the scarcity of labeled data in the early hours of a crisis hinders machine learning tasks thus delays crisis response. In this work, we propose to use an inductive semi-supervised technique to utilize unlabeled data, which is often abundant at the onset of a crisis event, along with fewer labeled data. Specif- ically, we adopt a graph-based deep learning framework to learn an inductive semi-supervised model. We use two real-world crisis datasets from Twitter to evaluate the proposed approach. Our results show significant improvements using unlabeled data as compared to only using labeled data.Comment: 5 pages. arXiv admin note: substantial text overlap with arXiv:1805.0515

COMPRESSED LEARNING FOR TEXT CATEGORIZATION

In text classification based on the bag-of-words (BoW) or similar representations, we usually have a large number of features, many of which are irrelevant (or even detrimental) for classification tasks. Recent results show that compressed learning (CL), i.e., learning in a domain of reduced dimensionality obtained by random projections (RP), is possible, and theoretical bounds on the test set error rate have been shown. In this work, we assess the performance of CL, based on RP of BoW representations for text classification. Our experimental results show that CL significantly reduces the number of features and the training time, while simultaneously improving the classification accuracy. Rather than the mild decrease in accuracy upper bounded by the theory, we actually find an increase of accuracy. Our approach is further compared against two techniques, namely the unsupervised random subspaces method and the supervised Fisher index. The CL approach is suited for unsupervised or semi-supervised learning, without any modification, since it does not use the class labels

Asymptotic Analysis of Generative Semi-Supervised Learning

Semisupervised learning has emerged as a popular framework for improving modeling accuracy while controlling labeling cost. Based on an extension of stochastic composite likelihood we quantify the asymptotic accuracy of generative semi-supervised learning. In doing so, we complement distribution-free analysis by providing an alternative framework to measure the value associated with different labeling policies and resolve the fundamental question of how much data to label and in what manner. We demonstrate our approach with both simulation studies and real world experiments using naive Bayes for text classification and MRFs and CRFs for structured prediction in NLP.Comment: 12 pages, 9 figure

An Exploration of Semi-supervised Text Classification

Author's accepted manuscriptGood performance in supervised text classification is usually obtained with the use of large amounts of labeled training data. However, obtaining labeled data is often expensive and time-consuming. To overcome these limitations, researchers have developed Semi-Supervised learning (SSL) algorithms exploiting the use of unlabeled data, which are generally easy and free to access. With SSL, unlabeled and labeled data are combined to outperform Supervised-Learning algorithms. However, setting up SSL neural networks for text classification is cumbersome and frequently based on a trial and error process. We show that the hyperparameter configuration significantly impacts SSL performance, and the learning rate is the most influential parameter. Additionally, increasing model size also improves SSL performance, particularly when less pre-processing data are available. Interestingly, as opposed to feed-forward models, recurrent models generally reach a performance threshold as pre-processing data size increases. This article expands the knowledge on hyperparameters and model size in relation to SSL application in text classification. This work supports the use of SSL work in future NLP projects by optimizing model design and potentially lowering training time, particularly if time-restricted.acceptedVersio

A novel two stage scheme utilizing the test set for model selection in text classification

Text classification is a natural application domain for semi-supervised learning, as labeling documents is expensive, but on the other hand usually an abundance of unlabeled documents is available. We describe a novel simple two stage scheme based on dagging which allows for utilizing the test set in model selection. The dagging ensemble can also be used by itself instead of the original classifier. We evaluate the performance of a meta classifier choosing between various base learners and their respective dagging ensembles. The selection process seems to perform robustly especially for small percentages of available labels for training