Code for the paper Biomedical Event Extraction with Hierarchical Knowledge Graphs

PlusLabNLP, updated 🕥 2022-12-08 10:47:05

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.

| Model | Dev Set F1 | Test Set F1 | | ------------- |-------------:| -----:| | SciBERT Baseline | 59.33 | 58.50 | | GEANet-SciBERT | 60.38 | 60.06 | | Previous SOTA | N/A | 58.65 |

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

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.
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

bibtex @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.", }

Issues

Bump certifi from 2020.4.5.2 to 2022.12.7

opened on 2022-12-08 10:47:05 by dependabot[bot]

Bumps certifi from 2020.4.5.2 to 2022.12.7.

Commits

9e9e840 2022.12.07
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939a28f 2022.09.14
aca828a 2022.06.15.2
de0eae1 Only use importlib.resources's new files() / Traversable API on Python ≥3.11 ...
b8eb5e9 2022.06.15.1
47fb7ab Fix deprecation warning on Python 3.11 (#199)
b0b48e0 fixes #198 -- update link in license
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Bump pillow from 7.1.2 to 9.3.0

opened on 2022-11-22 06:14:08 by dependabot[bot]

Bumps pillow from 7.1.2 to 9.3.0.

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Initialize libtiff buffer when saving #6699 [@radarhere]

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9.3.0 (2022-10-29)

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [wiredfool]

Initialize libtiff buffer when saving #6699 [radarhere]

Inline fname2char to fix memory leak #6329 [nulano]

Fix memory leaks related to text features #6330 [nulano]

Use double quotes for version check on old CPython on Windows #6695 [hugovk]

Remove backup implementation of Round for Windows platforms #6693 [cgohlke]

Fixed set_variation_by_name offset #6445 [radarhere]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [cgohlke]

Release Python GIL when converting images using matrix operations #6418 [hmaarrfk]

Added ExifTags enums #6630 [radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [radarhere]

Added support for reading BMP images with RLE4 compression #6674 [npjg, radarhere]

Decode JPEG compressed BLP1 data in original mode #6678 [radarhere]

Added GPS TIFF tag info #6661 [radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [radarhere]

Do not attempt normalization if mode is already normal #6644 [radarhere]

... (truncated)

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Bump mistune from 0.8.4 to 2.0.3

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Bump numpy from 1.16.0 to 1.22.0

opened on 2022-06-22 02:20:25 by dependabot[bot]

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v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

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Releases

CORD19 Biomedical Event Extraction First Release 2020-07-02 22:11:05

CORD19 Biomedical Event Extraction First Release

PlusLab

Peng's Language Understanding & Synthesis Lab at UCLA and USC

GitHub Repository Homepage

event-extraction biomedical covid-19 cord-19 natural-language-processing bert multitask-learning deep-learning

Code for the paper Biomedical Event Extraction with Hierarchical Knowledge Graphs

Biomedical Event Extraction with Hierarchical Knowledge Graphs

Introduction

CORD-19

Dependencies

Making predictions

On the CORD-19 dataset

On a single biomedical sentence

Project Structure

Citation

Issues

Bump certifi from 2020.4.5.2 to 2022.12.7

Bump pillow from 7.1.2 to 9.3.0

9.3.0

Changes

9.3.0 (2022-10-29)

Bump joblib from 0.14.1 to 1.2.0

Release 1.2.0

Release 1.1.0

Bump nbconvert from 5.6.1 to 6.5.1

Release 6.5.1

6.5.0

What's Changed

New Contributors

6.4.3

What's Changed

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6.4.0

What's Changed

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Bump mistune from 0.8.4 to 2.0.3

Version 2.0.2

Version 2.0.1

Version 2.0.0

Version 2.0.0 RC1

Version 2.0.0 Alpha 1

Changelog

Bump numpy from 1.16.0 to 1.22.0

v1.22.0

NumPy 1.22.0 Release Notes

Expired deprecations

Deprecated numeric style dtype strings have been removed

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

Releases

CORD19 Biomedical Event Extraction First Release 2020-07-02 22:11:05

PlusLab

Expired deprecations for `loads`, `ndfromtxt`, and `mafromtxt` in npyio