Neural Coref Models, as described in "Learning Global Features for Coreference Resolution", Sam Wiseman, Alexander M. Rush, and Stuart M. Shieber, NAACL 2016,
and
"Learning Anaphoricity and Antecedent Ranking Features for Coreference Resolution", Sam Wiseman, Alexander M. Rush, Stuart M. Shieber, and Jason Weston, ACL 2015.
Code for ``Learning Global Features for Coreference Resolution'' will be added soon!
For questions/concerns/bugs please contact swiseman@seas.harvard.edu.
This directory contains all the code necessary for duplicating our experiments. In particular, the modifiedBCS/ directory contains code for extracting features and for printing out predictions in CoNLL format, and the nn/ directory contains code for training models, saving them, and writing their predictions out. It also contains code for converting text feature files into hdf5 format, which is assumed by the nn/ models. Each of these subdirectories contains its own README; refer there for details. Finally, the run_experiments.py script in the current directory should reproduce our results. See it also for details on running the various pieces of the overall system.
The script run_experiments.py contains code and commands for duplicating our experiments, from feature extraction from the CoNLL files through training and evaluation. To run this script, you need to have extracted the CoNLL files from OntoNotes using the instructions at http://conll.cemantix.org/2012/data.html, which should give you a single top-level directory containing a hierarchy of CoNLL files.
Pre-extracted Basic+ features (for train, dev, and test) can be downloaded here: https://drive.google.com/folderview?id=0B1ytQXPDuw7OYng1SGhFR0hRcnM&usp=sharing
Saved Models (including antecedent and anaphoricity subtask networks, and g1 and g2 pre-trained networks) can be downloaded here: https://drive.google.com/folderview?id=0B1ytQXPDuw7OeU5ENnZXS0JsNGs&usp=sharing
Both the features and saved models are bzipped, and must be bunzipped before you can use them.
Copyright (c) 2015 Sam Wiseman. All Rights Reserved.
The code in this repository is covered a GNU GPL License. See LICENSE.txt.