Zero-Shot Transfer Learning for Event Extraction

Setup Instruction

This repository is built on Theano (=0.8, verifed. Issues may be reported on recent versions), Python 2.7. The environment can be quickly set up by running

pip install -r requirements.txt

Run the Model

Step 1

Preprocess Data: remove XML tags from ACE/ERE articles, sentence merging, sentence segmantation and tokenization.

java -jar removeXmlTag.jar [source_path] [source_without_tag_path]

python rsd2ltf.py [source_without_tag_path] [ltf_path] --extension .txt --seg_option nltk+linebreak

java -jar sentenceExtractor.jar [ltf_path] [sent_path] [sent_id_path]

[source_path]: the path for input files (all .sgm files from ACE source corpus)

[source_without_tag_path]: output path after merging sentences and removing xml tags from ACE source articles

[ltf_path]: output path after sentence segmentation and tokenization

[sent_path] [sent_id_path]: output paths for sentences and sentence ids

Step 2

Apply AMR parser (https://github.com/c-amr/camr, or https://github.com/jflanigan/jamr) to parse each single doc, and get aligned parsing output [amr_parsing_path].

Step 3

Extract event mention structures from AMR parsing outputs

java -jar amrPostProcessing.jar [amr_parsing_path] [sent_id_path] [resource/amrRelationsAnnotated.txt] [resource/frameNetVN.txt] [trigger_structure_file]

[amr_parsing_path]: path of AMR parsing files generated from Step2

[sent_id_path]: path of sentence ids generated from Step1

[resource/amrRelationsAnnotated.txt]: path for resource/amrRelationsAnnotated.txt file

[resource/frameNetVN.txt]: path for resource/frameNetVN.txt file