In this tutorial, we present the fundamentals behind the next version of the Perspective API from Google Jigsaw. The approach to the challenge is grounded in the following principles:
When possible, we rely on state of the art self-supervised pretraining approaches to learn text embeddings and language models.
We deploy a character-level multilingual model that is applicable across many languages, domains, and tasks.
We present a new evaluation methodology for code-switching and weak supervision.
To this end, we review the fundamentals of text representations. We describe different ways of learning representations for text, including unsupervised and distantly-supervised methods for building representations from unlabeled data. We then move to more specific topics such as multilingual embeddings, domain-specific text representations, and multilingual text representations. Finally, we turn our attention to specific applications of text representations in a range of tasks, including text similarity, sentiment analysis, question answering, topic discovery, concept extraction, and text classification.
The ever-growing amount of social media data raised a need for developing scalable and efficient content detection systems. One of the most interesting and versatile classes of content detection systems is the class of toxicity detectors. Such systems can be used by a broad range of applications, including generating reports on the toxicity of comments or threads, detecting and blocking offensive or harassing content, and identifying potential hate speech. The content detection task is made challenging by the lack of annotated data, and the constantly evolving nature of social media.
This course introduces the fundamentals of probability and statistics, as well as data analysis, machine learning and graphics. New to this course are the topics of natural language processing and neural networks. This course also introduces the practice of programming. We introduce a special emphasis on real-world data, and the applicability of models and methods to a variety of problems in biomedical imaging, molecular biophysics, and epidemiology.
The data is collected from across a range of disciplines, including genomics, genetics, physics, neurology, epidemiology and modern physics. The topics include understanding the role of machine learning and statistics in the analysis of this data, as well as the application of such methods to identify novel biomarkers, identify the new properties of proteins, understand the novel phenomena in neuroscience, and to develop novel methods to understand the impact of novel drugs.
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