We present a holistic approach to building a robust and useful natural language classification system for real-world content moderation. The success of such a system relies on a chain of carefully designed and executed steps, including the design of content taxonomies and labeling instructions, data quality control, an active learning pipeline to capture rare events, and a variety of methods to make the model robust and to avoid overfitting. Our moderation system is trained to detect a broad set of categories of undesired content, including sexual content, hateful content, violence, self-harm, and harassment. This approach generalizes to a wide range of different content taxonomies and can be used to create high-quality content classifiers that outperform off-the-shelf models.

In the United States, about 600,000 people die of heart disease every year. The annual cost of care services, medications, and lost productivity reportedly exceeds 108.9 billion dollars. Effective disease risk assessment is critical to prevention, care, and treatment planning. Recent advancements in text analytics have opened up new possibilities of using the rich information in electronic medical records (EMRs) to identify relevant risk factors. The 2014 i2b2/UTHealth Challenge brought together researchers and practitioners of clinical natural language processing (NLP) to tackle the identification of heart disease risk factors reported in EMRs. We participated in this track and developed an NLP system by leveraging existing tools and resources, both public and proprietary. Our system was a hybrid of several machine-learning and rule-based components. The system achieved an overall F1 score of 0.9185, with a recall of 0.9409 and a precision of 0.8972.