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Part 0 - Setup
Covering Installation and Setup (including best practices)
Series Tracker
AllenNLP the Hard Way
This series is my AllenNLP tutorial that goes from installation through building a state-of-the-art (or nearly) named entity recognizer. By the end we will have covered dataset readers, Hierarchical LSTMs and BERT, predictors, debugging, and experimental repeatability.
All the code for the tutorial can be found in the associated Github repository.
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Part 1 - Building a Dataset Reader
An introduction to Named Entity Recognition (NER) and the CoNLL'03 Dataset.
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Part 2 - Building a Baseline Model
Building a sequence-tagging LSTM for Named Entity Recognition (NER).
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Part 3 - Configuring Experiments
Combining the dataset readers and models to run our first experiment.
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Part 4 - Tackling Your Own Experiments
Recommendations on how to tackle your own experiments.
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Part 5 - Building a Predictor
Exploring AllenNLP Predictors for using your trained models.
Literature Review
This series contains summaries of interesting papers.
J
Jointly Learning Sentence Embeddings and Syntax with Unsupervised Tree-LSTMs
Summary of paper by Jean Maillard, Stephen Clark, and Dani Yogatama