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Hierarchical Modular Event Argument Extraction

The code is an implementation of Hierarchical Modular Event Argument Extraction (EMNLP19 paper).

Requirments

tensorflow-gpu==1.10

stanfordcorenlp (see https://github.com/Lynten/stanford-corenlp for detail)

numpy

tqdm

Usage

To run this code, you need to:

  1. put English folder of ACE05 dataset into ./, or you can modify path in constant.py. (You can get ACE2005 dataset here: https://catalog.ldc.upenn.edu/LDC2006T06)
  2. put stanford language model (stanford-corenlp-full-2018-10-05) into ./, or you can modify path in constant.py. (You can download here: https://stanfordnlp.github.io/CoreNLP/history.html)
  3. put GloVe embedding file into ./glove folder, or you can modify path in constant.py. (You can download GloVe embedding here: https://nlp.stanford.edu/projects/glove/)
  4. Run python train.py --gpu 0 --mode HMEAE to run with HMEAE model. Run python train.py --gpu 0 --mode DMCNN to run with DMCNN model.

All parameters are in constant.py, you can modify them as you wish.

Dataset

Due to license limitation, we can't distribute datasets directly, please download the dataset by yourself. The download link is given in Usage part.

The code will automatically extract information of ACE2005 dataset and dumps them into json format(train.json ,dev.json and test.json) into path ACE_DUMP in constant.py. This is implented in class Extractor in utils.py.

Each file is composed of a list, which elements are instances with following format:

{
    "tokens": XX,           #tokens of a sentence, a list with string elements
    "start": XX,            #starting offsets of the sentence in original files, an integer
    "end": XX,              #ending offsets of the sentence in original files, an integer
    "offsets":XX,           #offsets of each tokens, a list with tuple elements
    "trigger_tokens":XX,    #tokens of trigger words, a list with string elements
    "trigger_start":XX,     #start index of trigger words of tokens, an integer
    "trigger_end":XX,       #end index of trigger words of tokens, an integer
    "trigger_offsets":XX,   #offsets of trigger words, a list with tuple elements
    "event_type":XX,        #event type of tokens with given triggers, a string
    "file":XX,              #file name without suffix
    "dir":XX,               #dir name
    "entities":XX           #entitie in this sentencem, a list with entity elements
}

Each entity is a dictionary with following format:

{
    "token":XX,             #tokens of the entity, a list with string elements
    "role":XX,              #role of the entity when trigger is given, a string
    "offsets":XX,           #offsets of entity, a list with tuple elements
    "start":XX,             #start offset of entity, an integer
    "end":XX,               #snd offset of entity, an integer
    "idx_start":XX,         #start index in tokens, an integer
    "idx_end":XX            #end index in tokens, an integer
}

Cite

If the codes help you, please cite our paper:

HMEAE: Hierarchical Modular Event Argument Extraction. XiaoZhi Wang, Ziqi Wang, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Maosong Sun, Jie Zhou, Xiang Ren. EMNLP-IJCNLP 2019.

About

Source code for EMNLP-IJCNLP 2019 paper "HMEAE: Hierarchical Modular Event Argument Extraction".

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