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Extracting Chemical-Disease Relations from Academic Literature

In this advanced tutorial, we will build a Snorkel application to tackle the more challenging task of identifying mentions of a chemical causing a disease from academic literature. The task is inspired by the BioCreative CDR task from 2015, which is also where the data is derived from. The tutorial is broken up into three notebooks, each covering a step in the pipeline:

  1. Preprocessing and Candidate Extraction [CDR_Tutorial_1]: First we parse the raw input documents into contexts (documents, sentences), and extract consituent linguistic attributes. We also use matcher operators to extract sets of candidate chemical-disease relation mentions from the preprocessed input using entity tags from an automated tool.

  2. Developing and Modeling Labeling Functions [CDR_Tutorial_2]: We develop a more advanced set of labeling functions, based on text patterns and distant supervision. These labeling functions also have dependencies between them, and we model these automatically. Finally, we train a generative model with the learned dependencies.

  3. Learning an Extraction Model [CDR_Tutorial_3]: Using the noise-aware training label set we generated in the second notebook, we learn an end exraction model. Here, we'll use an LSTM neural network rather than a linear model as in the intro tutorial.


For example, in the sentence

Warfarin-induced artery calcification is accelerated by growth and vitamin D.

our goal is to extract the chemical-disease relation pair ("warfarin", "artery calcification").