yueyedeai / graphlstm_release

Implementation of TACL 2017 paper: Cross-Sentence N-ary Relation Extraction with Graph LSTMs. Nanyun Peng, Hoifung Poon, Chris Quirk, Kristina Toutanova and Wen-tau Yih.

Cross-Sentence N-ary Relation Extraction with Graph LSTMs

This is the data and source code of the papers:

Cross-Sentence N-ary Relation Extraction with Graph LSTMs Nanyun Peng, Hoifung Poon, Chris Quirk, Kristina Toutanova and Wen-tau Yih Transactions of the Association for Computational Linguistics, Vol 5, 2017

If you use the code, please kindly cite the following bibtex:

@article{peng2017cross,
title={Cross-Sentence N-ary Relation Extraction with Graph LSTMs},
author={Peng, Nanyun and Poon, Hoifung and Quirk, Chris and Toutanova, Kristina and Yih, Wen-tau},
journal={Transactions of the Association for Computational Linguistics},
volume={5},
pages={101--115},
year={2017}
}

Data

File system hierarchy:

• data/
  • drug_gene_var/
    • 0/
      • data_graph
      • sentences_2nd
      • graph_arcs
    • 1/
    • 2/
    • 3/
    • 4/
  • drug_var/
    • the same structure as in drug_gene_var
  • drug_gene/
    • the same structure as in drug_gene_var

Source attribution:

The full information of the instances are contained in the file "data_graph", it's a json format file containing information such as PubMed articleID, paragraph number, sentence number, and the information about the tokens including part-of-speech tags, dependencies, etc. produced by Stanford coreNLP tool.

Preprocessing

We processed the source data into the format that is easier for our code to consume, which includes two files: "sentences_2nd" and "graph_arcs". The "sentences_2nd" file contains the information of the raw input, and the format is:

the-original-sentencesindices-to-the-first-entity(drug)indices-to-the-second-entity(gene/variant)[indices-to-the-third-entity(variant)]relation-label

The "graph_arcs" file contains the information of the dependencies between the words, including time sequence adjacency, syntactic dependency, and discourse dependency. The format is:

dependencies-for-node-0dependencies-for-node-1... dependencies-for-node-n = dependency-0,,,dependency-1... dependency-n = dependency-type::dependent-node

Experiments

To reproduce the results in our paper, the script ./scripts/batch_run_lstm.sh contains the command for running all the cross-validation folds for both drug-gene-variant triple and drug-variant binary relations.

The script ./scripts/batch_run_multitask.sh contains the command for running all the multi-task learning experiments.