amr_coref

A python library / model for creating co-references between AMR graph nodes.

Install

To install:

pip install zensols.amr_coref

About

amr_coref is a python library and trained model designed to do co-referencing between Abstract Meaning Representation graphs.

The project follows the general approach of the neuralcoref project and it's excellent blog on the co-referencing. However, the model is trained to do direct co-reference resolution between graph nodes and does not depend on the sentences the graphs were created from.

The trained model achieves the following scores

MUC   :  R=0.647  P=0.779  F₁=0.706
B³    :  R=0.633  P=0.638  F₁=0.630
CEAF_m:  R=0.515  P=0.744  F₁=0.609
CEAF_e:  R=0.200  P=0.734  F₁=0.306
BLANC :  R=0.524  P=0.799  F₁=0.542
CoNLL-2012 average score: 0.548

Project Status

!! The following papers have GitHub projects/code that are better scoring and may be a preferable solution. See the uploaded file in #1 for a quick view of scores.

• VGAE as Cheap Supervision for AMR Coreference Resolution
• End-to-end AMR Coreference Resolution

This is a fork of Brad Jascob's amr_coref repository, and modified to address the multiprocessing issues on non-Debian style OSs. See #3 for details on the issue.

Usage

To turn multi-threading off, create the Interface instance with use_multithreading=False.

Installation and usage

There is currently no pip installation. To use the library, simply clone the code and use it in place.

The pre-trained model can be downloaded from the assets section in releases.

To use the model create a data directory and un-tar the model in it.

The script 40_Run_Inference.py, is an example of how to use the model.

Training

If you'd like to train the model from scratch, you'll need a copy of the AMR corpus. To complete training, run the scripts in order.

• 10_Build_Model_TData.py
• 12_Build_Embeddings.py
• 14_Build_Mention_Tokens.py
• 30_Train_Model.py.

You'll need amr_annotation_3.0 and GloVe/glove.6B.50d.txt in your data directory

The first few scripts will create the training data in data/tdata and the model training script will create data/model. Training takes less than 4 hours.