Biomedical Event Extraction with Hierarchical Knowledge Graphs

Introduction

This repo hosts the code for the paper Biomedical Event Extraction with Hierarchical Knowledge Graphs. We represent knowledge from UMLS in hierarchical knowledge graphs, and integrate knowledge into pre-trained contextual representations with a proposed graph neural networks, Graph Edge-conditioned Attention Neural Networks (GEANet). Currently, only SciBERT Baseline is available, and our best performing model GEANet-SciBERT will soon be released.

CORD-19

With the increasing concern about the COVID-19 pandemic, researchers have been putting much effort in providing useful insights into the COVID-19 Open Research Dataset Challenge (CORD-19). This repo also demonstrates how we extract biomedical events with SciBERT, a BERT trained on scientific corpus, which was fine-tuned on the GENIA BioNLP shared task 2011. We took reference from the pipeline described in Bjorne et al., where the pipeline can be broken into 3 stages: trigger detection, edge/argument detection and unmerging. The extracted events can be found here.

In addition, we adopted the framework from Han et al., where trigger and edge detections are trained in a multitask setting.

Dependencies

Our experiments were ran with Python 3.6.10, PyTorch 1.4.0 and CUDA 10.1 on a CentOS machine. Please follow the instructions of GPU dependencies and usage as illustrated in the official PyTorch website. We utilized the NER model en_ner_jnlpba_md provided by ScispaCy for tagging biomedical entities.

Run pip install -r requirements to install all the required packages.

(Optional) If you would like to use the knowledge incorporation component, additional packages are required for Pytorch Geometric can be installed as follows:

$ pip install torch-scatter==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.4.0.html
$ pip install torch-sparse==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.4.0.html
$ pip install torch-cluster==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.4.0.html
$ pip install torch-spline-conv==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.4.0.html

, where ${CUDA} should be replaced by your installed CUDA version. (e.g. cpu, cu92, cu101, cu102)

The trained model can be downloaded from here. You need to unzip decompress it before running the program.

Making predictions

On the CORD-19 dataset

1. Run through Preprocess CORD.ipynb to generate the processed .txt files to genia_cord_19 from the json files in the custom_license/custom_license/pmc_json/ directory.
2. Type . run_multitask_bert.sh in the terminal to run the whole event extraction pipeline and generate event annotations into genia_cord_19_output.

On a single biomedical sentence

Call the biomedical_evet_extraction function in the predict.py script. This function takes in a single string as parameter. The event extraction pipeline will be run on this string.

>>> biomedical_evet_extraction("BMP-6 inhibits growth of mature human B cells; induction of Smad phosphorylation and upregulation of Id1.")
>>> [{'tokens': ['BMP-6', 'inhibits', 'growth', 'of', 'mature', 'human', 'B', 'cells', ';', 'induction', 'of', 'Smad', 'phosphorylation', 'and', 'upregulation', 'of', 'Id1', '.'], 'events': [{'event_type': 'Positive_regulation', 'triggers': [{'event_type': 'Positive_regulation', 'text': 'upregulation', 'start_token': 14, 'end_token': 14}], 'arguments': [{'role': 'Theme', 'text': 'Id1', 'start_token': 16, 'end_token': 16}]}], 'ner': [[0, 0, 'PROTEIN'], [4, 7, 'CELL_TYPE'], [11, 11, 'PROTEIN'], [16, 16, 'PROTEIN']]}]

Project Structure

├── genia_cord_19
├── genia_cord_19_output
├── preprocessed_data
├── eval
└── weights

Citation

@inproceedings{huang-etal-2020-biomedical,
    title = "Biomedical Event Extraction with Hierarchical Knowledge Graphs",
    author = "Huang, Kung-Hsiang  and
      Yang, Mu  and
      Peng, Nanyun",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.114",
    doi = "10.18653/v1/2020.findings-emnlp.114",
    pages = "1277--1285",
    abstract = "Biomedical event extraction is critical in understanding biomolecular interactions described in scientific corpus. One of the main challenges is to identify nested structured events that are associated with non-indicative trigger words. We propose to incorporate domain knowledge from Unified Medical Language System (UMLS) to a pre-trained language model via Graph Edge-conditioned Attention Networks (GEANet) and hierarchical graph representation. To better recognize the trigger words, each sentence is first grounded to a sentence graph based on a jointly modeled hierarchical knowledge graph from UMLS. The grounded graphs are then propagated by GEANet, a novel graph neural networks for enhanced capabilities in inferring complex events. On BioNLP 2011 GENIA Event Extraction task, our approach achieved 1.41{\%} F1 and 3.19{\%} F1 improvements on all events and complex events, respectively. Ablation studies confirm the importance of GEANet and hierarchical KG.",
}