This coreference resolution module is based on the super fast spaCy parser and uses the neural net scoring model described in Deep Reinforcement Learning for Mention-Ranking Coreference Models by Kevin Clark and Christopher D. Manning, EMNLP 2016. With ✨Neuralcoref v2.0, you should now be able to train the coreference resolution system on your own dataset — e.g., another language than English! — provided you have an annotated dataset.
from neuralcoref import Coref coref = Coref() clusters = coref.one_shot_coref(utterances=u"She loves him.", context=u"My sister has a dog.") mentions = coref.get_mentions() utterances = coref.get_utterances() resolved_utterance_text = coref.get_resolved_utterances()
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If you have a project that you want the spaCy community to make use of, you can suggest it by submitting a pull request to the spaCy website repository. The Universe database is open-source and collected in a simple JSON file. For more details on the formats and available fields, see the documentation. Looking for inspiration your own spaCy plugin or extension? Check out the
project idea label on the issue tracker.