RELVM

This repository contains the code accompanying the paper "Learning Informative Representations of Biomedical Relations with Latent Variable Models", Harshil Shah and Julien Fauqueur, EMNLP SustaiNLP 2020, (https://arxiv.org/abs/2011.10285).

Requirements

• Python 3.7
  • Numpy >= 1.17.2
  • Tensorflow >= 2.0.0

Instructions

Introduction

The code in this repository is for training a latent variable generative model of pairs of entities and the contexts (i.e. sentences) in which the entities occur. The representations from this model can then be used to perform both mention-level and pair-level classification.

Throughout the code, the following conventions are used:

• x or entities_x will refer to the first entity in a context.
• y or entities_y will refer to the second entity in a context.
• c or contexts will refer to the context (i.e. sentence) in which the entities occur.
• r or labels will refer to the class label when performing either mention-level or pair-level classification.

Data

To avoid out-of-memory issues, the data is stored in memory-mapped Numpy arrays. The metadata is stored in JSON files.

Unsupervised

The unsupervised data directory (e.g. data/unsupervised) should contain the following JSON files (which contain the metadata):

• vocab.json
  • This is a list of strings. It is the set of possible tokens which can appear in the contexts.
• entity_types.json
  • This is a list of strings. It is the set of possible values for the entities. If training the model for mention-level classification, this will be a list of entity types. If training the model for pair-level classification, this will be a list of entity identifiers.

It should also contain the following memory-mapped Numpy arrays:

• entities_x.mmap
  • This is a one-dimensional array. The i^th row contains the index to entity_types.json for the first entity in the i^th context.
• entities_y.mmap
  • This is a one-dimensional array. The i^th row contains the index to entity_types.json for the second entity in the i^th context.
• contexts.mmap
  • This is a two-dimensional array. The i^th row contains the indices to vocab.json for the i^th context.

Mention-level classification

The mention-level classification data directory (e.g. data/supervised/mention_level) should contain the following JSON files (which contain the metadata):

• label_types.json
  • This is a list of strings. It is the set of possible values for the labels.

It should also contain the following memory-mapped Numpy arrays (for the training, validation, and test data respectively):

• entities_x_{train,valid,test}.mmap
  • These are one-dimensional arrays. The i^th row contains the index to entity_types.json from the unsupervised data directory for the first entity in the i^th context.
• entities_y_{train,valid,test}.mmap
  • These are one-dimensional arrays. The i^th row contains the index to entity_types.json from the unsupervised data directory for the second entity in the i^th context.
• contexts_{train,valid,test}.mmap
  • These are two-dimensional arrays. The i^th row contains the indices to vocab.json from the unsupervised data directory for the i^th context.
• labels_{train,valid,test}.mmap
  • These are one-dimensional arrays. The i^th row contains the index to label_types.json for the i^th entity pair and context.

Pair-level classification

The pair-level classification data directory (e.g. data/supervised/pair_level) should contain the following JSON files (which contain the metadata):

• label_types.json
  • This is a list of strings. It is the set of possible values for the labels.
• pos_entity_pairs.json
  • This is a list of strings containing the entity pairs which have a positive relation. Each element of this list contains the unique entity identifiers for the pair, joined together with a :.

It should also contain the following memory-mapped Numpy arrays (for the training, validation, and test data respectively):

• entities_x_{train,valid,test}.mmap
  • These are one-dimensional arrays. The i^th row contains the index to entity_types.json from the unsupervised data directory for the first entity in the i^th pair.
• entities_y_{train,valid,test}.mmap
  • These are one-dimensional arrays. The i^th row contains the index to entity_types.json from the unsupervised data directory for the second entity in the i^th pair.
• labels_{train,valid,test}.mmap
  • These are one-dimensional arrays. The i^th row contains the index to label_types.json for the i^th entity pair.

Training and evaluating

Unsupervised representation learning

To train the unsupervised representation model, run exp_unsup.py, specifying the directory in which to store the model parameters. For example:

mkdir -p exp_outputs/unsup
python3 exp_unsup.py exp_outputs/unsup

Mention-level classification

Once the unsupervised representation model has been trained, the mention-level classification model can be trained and evaluated by running exp_classification_mention.py. First, the following variables in exp_classification_mention.py must be set:

• trainer_unsup_pre_trained_dir must be set to the directory with the saved parameters from the unsupervised representation model (e.g. exp_outputs/unsup).
• unsup_data_dir must be set to the directory with the data used to train the unsupervised representation model (e.g. data/unsupervised).

When running exp_classification_mention.py, the directory in which to store the classification model parameters must be specified. For example:

mkdir -p exp_outputs/classification_mention
python3 exp_classification_mention.py exp_outputs/classification_mention

Pair-level classification

The pair-level classification model can be trained and evaluated by running exp_classification_pair.py in an identical fashion to the mention-level classification model. Again, the following variables in exp_classification_pair.py must be set:

• trainer_unsup_pre_trained_dir must be set to the directory with the saved parameters from the unsupervised representation model (e.g. exp_outputs/unsup).
• unsup_data_dir must be set to the directory with the data used to train the unsupervised representation model (e.g. data/unsupervised).

When running exp_classification_pair.py, the directory in which to store the classification model parameters must be specified. For example:

mkdir -p exp_outputs/classification_pair
python3 exp_classification_pair.py exp_outputs/classification_pair

Component testing

From the project root folder, the following 3 scripts should be run in this order and all return OK.

python3 tests/test_unsup.py
python3 tests/test_classification_mention.py
python3 tests/test_classification_pair.py