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Text Classification

Text Classification with Simple Transformers


Using Transformer models has never been simpler! Yes that's what Simple Transformers author Thilina Rajapakse says and I agree with him so should you. You might have seen lengthy code with hundreds of lines to implement transformers models such as BERT, RoBERTa, etc. Once you understand how to use Simple Transformers you will know how easy and simple it is to use transformer models. TheSimple Transformers library is built on top of Hugging Face Transformers library. Hugging Face Transformers provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, T5, etc.) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) and provides more than thousand pre-trained models and covers around 100 languages.

Box adds automated classification to content security product, Box Shield


Box on Tuesday started rolling out a new automated classification feature for Box Shield, its popular content security product. The new feature uses machine learning to automatically scan files as they're uploaded or edited in Box and apply classification labels. Box stressed that the feature should better help organizations meet compliance needs, even as employees work remotely through the COVID-19 pandemic. "Remote work has accelerated cloud adoption as businesses seek enable a distributed workforce and serve their customers digitally," Box CISO Lakshmi Hanspal said in a statement. "This requires completely new approach to security and privacy. As more work is done outside office boundaries on both managed and personal devices it is critical to have one source of truth for all of your data in order to meet new regulatory and compliance standards without slowing down business."

Text Classification with NLP: Tf-Idf vs Word2Vec vs BERT


In this article, using NLP and Python, I will explain 3 different strategies for text multiclass classification: the old-fashioned Bag-of-Words (with Tf-Idf), the famous Word Embedding (with Word2Vec), and the cutting edge Language models (with BERT). NLP (Natural Language Processing) is the field of artificial intelligence that studies the interactions between computers and human languages, in particular how to program computers to process and analyze large amounts of natural language data. NLP is often applied for classifying text data. Text classification is the problem of assigning categories to text data according to its content. There are different techniques to extract information from raw text data and use it to train a classification model.

Text Classification using Neural Networks


Understanding how chatbots work is important. A fundamental piece of machinery inside a chat-bot is the text classifier. Let's look at the inner workings of an artificial neural network (ANN) for text classification.

Document Classification


Document or text classification is one of the predominant tasks in Natural language processing. It has many applications including news type classification, spam filtering, toxic comment identification, etc. In big organizations the datasets are large and training deep learning text classification models from scratch is a feasible solution but for the majority of real-life problems your dataset is small and if you want to build your machine learning model you need to be smart. In this article (originally posted by Shahul Es on the, I will talk about pragmatic approaches towards text representation which make document classification on small datasets doable.

Weight Poisoning Attacks on Pre-trained Models Machine Learning

Recently, NLP has seen a surge in the usage of large pre-trained models. Users download weights of models pre-trained on large datasets, then fine-tune the weights on a task of their choice. This raises the question of whether downloading untrusted pre-trained weights can pose a security threat. In this paper, we show that it is possible to construct ``weight poisoning'' attacks where pre-trained weights are injected with vulnerabilities that expose ``backdoors'' after fine-tuning, enabling the attacker to manipulate the model prediction simply by injecting an arbitrary keyword. We show that by applying a regularization method, which we call RIPPLe, and an initialization procedure, which we call Embedding Surgery, such attacks are possible even with limited knowledge of the dataset and fine-tuning procedure. Our experiments on sentiment classification, toxicity detection, and spam detection show that this attack is widely applicable and poses a serious threat. Finally, we outline practical defenses against such attacks. Code to reproduce our experiments is available at

VGCN-BERT: Augmenting BERT with Graph Embedding for Text Classification Machine Learning

Much progress has been made recently on text classification with methods based on neural networks. In particular, models using attention mechanism such as BERT have shown to have the capability of capturing the contextual information within a sentence or document. However, their ability of capturing the global information about the vocabulary of a language is more limited. This latter is the strength of Graph Convolutional Networks (GCN). In this paper, we propose VGCN-BERT model which combines the capability of BERT with a Vocabulary Graph Convolutional Network (VGCN). Local information and global information interact through different layers of BERT, allowing them to influence mutually and to build together a final representation for classification. In our experiments on several text classification datasets, our approach outperforms BERT and GCN alone, and achieve higher effectiveness than that reported in previous studies.

Deep Learning Based Text Classification: A Comprehensive Review Machine Learning

Deep learning based models have surpassed classical machine learning based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this work, we provide a detailed review of more than 150 deep learning based models for text classification developed in recent years, and discuss their technical contributions, similarities, and strengths. We also provide a summary of more than 40 popular datasets widely used for text classification. Finally, we provide a quantitative analysis of the performance of different deep learning models on popular benchmarks, and discuss future research directions.

In Automation We Trust: Investigating the Role of Uncertainty in Active Learning Systems Machine Learning

We investigate how different active learning (AL) query policies coupled with classification uncertainty visualizations affect analyst trust in automated classification systems. A current standard policy for AL is to query the oracle (e.g., the analyst) to refine labels for datapoints where the classifier has the highest uncertainty. This is an optimal policy for the automation system as it yields maximal information gain. However, model-centric policies neglect the effects of this uncertainty on the human component of the system and the consequent manner in which the human will interact with the system post-training. In this paper, we present an empirical study evaluating how AL query policies and visualizations lending transparency to classification influence trust in automated classification of image data. We found that query policy significantly influences an analyst's trust in an image classification system, and we use these results to propose a set of oracle query policies and visualizations for use during AL training phases that can influence analyst trust in classification.

Seeing The Whole Patient: Using Multi-Label Medical Text Classification Techniques to Enhance Predictions of Medical Codes Machine Learning

Machine learning-based multi-label medical text classifications can be used to enhance the understanding of the human body and aid the need for patient care. We present a broad study on clinical natural language processing techniques to maximise a feature representing text when predicting medical codes on patients with multi-morbidity. We present results of multi-label medical text classification problems with 18, 50 and 155 labels. We compare several variations to embeddings, text tagging, and pre-processing. For imbalanced data we show that labels which occur infrequently, benefit the most from additional features incorporated in embeddings. We also show that high dimensional embeddings pre-trained using health-related data present a significant improvement in a multi-label setting, similarly to the way they improve performance for binary classification. High dimensional embeddings from this research are made available for public use.