wd-stanford-coref

This model was developed in order to extract Stanford coref 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 CoreNLP format then feed to the stanford pipeline while skipping tokenization.

For AllenNLP/SpaCy coreference resolution in python, you can find in this repo

Include implementation/example for extracting ECB+ corpus coref information

Pre-Requirements

• Java 1.8
• Gradle
• ECB+ corpus for running WD from ECB+ (Download ECB+)

Build And Run Within Doc coref from ECB+

• Clone the repo

• From command line navigate to project root directory and run:

  ./gradlew clean buildCorefJar

Should get a message saying: BUILD SUCCESSFUL in 25s

• Then run command

  java --add-modules java.se.ee -Xms4096m -Xmx8192m -jar build/libs/stanford-coref-1.0-SNAPSHOT.jar -corpus=ECB+ -output=output/ecb_wd_coref.json -threads=4

Arguments:

• -corpus: the path location of corpus folder (eg. ECB+)
• -output: file to save the json wd coref into
• -threads: set number of threads to run with (Default=2)

Experiment with other corpus

• Clone the repo
• Inherit IDataLoader and create a new DataLoader for parsing your corpus (see EcbDataLoader for example)
• replace IDataLoader in ExtractStanfordCoref, 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