wd-plus-srl-extraction

This model was developed in order to extract SpaCy/AllenNLP coref and SRL from already tokenized corpus, in order to avoid aligning model output tokenization with corpus tokenization (which are usually different). im using the corpus tagged data (tokens, sentences,...) to create the tokenized data in the SpaCy format then feed to the SRL/coref model while skipping model tokenization pipeline.

Include implementation/example for extracting ECB+ corpus SRL/coref information

Overview

Current Implementations:

• AllenNLP Coreference Resolution: For extracting AllenNLP within document coreference
• AllenNLP Semantic Role Labeling: For extracting AllenNLP SRL relations
• SpaCy Coreference Resolution: For extracting SpaCy within document coreference
• WEC-Eng Cross-Doc Coreference Resolution (experimental): For extracting cross-document coreference (more details here)
• Stanford Coreference Resolution: Can be found in this link

Pre-Requirements

• Python 3.6 or higher
• allennlp
• spacy
• Download Within doc SpaCy model from neuralcoref
• if running ECB+ corpus, download from (Download ECB+)
• download WEC-Eng pre-trained model from here

Install

pip install -r requirements.txt

Run

Allen Coreference

coref_allen.py Generate within document co-reference using AllenNLP

python src/coref_allen.py --input_file=ECB+ --output_file=output/allen_wd_coref.json

Allen SRL

srl_allen.py Generate semantic role labeling using AllenNLP

python src/srl_allen.py --ecb_root_path=ECB+/1 --output_file=output/allen_srl.json

• In order to read the json file back to objects use srl_allen.read_srl_json(input_file) method

spaCy Coreference

coref_spacy.py Generate within document co-reference using neuralcoref

python src/coref_spacy.py --input_file=ECB+ --output_file=output/spacy_wd_coref.json --model=en_coref_lg

WEC CD Coreference

coref_wec_model.py Generate within document and cross-document coreference using AllenNlp coref and WEC-Eng models.
This script has been used to generate the entity cross-document coreference facets in IFacetSum.

Using allen coref to predict and cluster within document coreference, then use WEC model to predict and generate cross document clusters.

1. checkout cross-doc-event-coref github repository

2. pip install -e cross-doc-event-coref

3. Run coref script:

   python src/coref_wec_model.py --input_file=input/Steroid_Use_docs_formatted.json --output_file=output/Steroid_allen_wd_coref_duc.json --loader=duc --model_file=model/wec_model --cuda=False

Experiment with other corpus

• Clone the repo
• Inherit IDataLoader and create a new DataLoader for parsing your corpus (see EcbDataLoader for example)
• replace DataLoader in required model main() method

Output

Output is in a json format, containing a list of within document coreference mentions:

[
    {
        "coref_chain": "0",
        "doc_id": "36_5ecb.xml",
        "sent_id": 4,
        "tokens_number": [
            1,
            2
        ],
        "tokens_str": "Mr. Blackmore"
    },
    {
        "coref_chain": "0",
        "doc_id": "36_5ecb.xml",
        "sent_id": 4,
        "tokens_number": [
            7
        ],
        "tokens_str": "he"
    },
    .
    .
    .
]

Where:

json field	Value	comment
coref_chain	Text	The mention coref cluster id
doc_id	Text	the document this mention belong to
sent_id	int	Mention original document sentence ID
tokens_number	List[int]	Mention span (text phrase as set in tokens_str) original tokens ids
tokens_str	String	The mention/span phrase