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experiments Public release May 15, 2018
morphology Public release May 15, 2018
scripts Public release May 15, 2018
.gitignore Initial commit with gitignore Apr 10, 2018 Updating readme Jul 10, 2018
config.yaml Public release May 15, 2018 Public release May 15, 2018

Derivational Morphology

This is the code repository for the ACL 2018 paper A Distributional and Orthographic Aggregation Model for English Derivational Morphology. If you use this code for your work, please cite

  author = 	"Deutsch, Daniel
		and Hewitt, John
		and Roth, Dan",
  title = 	"A Distributional and Orthographic Aggregation Model for English Derivational Morphology",
  booktitle = 	"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
  year = 	"2018",
  publisher = 	"Association for Computational Linguistics",
  pages = 	"1938--1947",
  location = 	"Melbourne, Australia",
  url = 	""


To clone the repository with the dataset, shard the training data, and download the unigram-counts.txt data file, run


The data/unigram-counts.txt file is stored here, but can be optionally reproduced with the following command

sh scripts/ download data/unigram-counts.txt

The distributional model uses pretrained word vectors from Please download the word vectors from here to data/GoogleNews-vectors-negative300.bin.gz.

Reproducing experiments

Our experiments are built on the Sun Grid Engine and use the qsub command to run several random seed restarts simultaneously.

To reproduce the accuracy and edit-distance results

sh scripts/ unconstrained experiments/default.yaml
sh scripts/ constrained experiments/constrained.yaml

python scripts/ unconstrained unconstrained-metrics.txt
python scripts/ constrained constrained-metrics.txt
python scripts/ unconstrained-metrics.txt constrained-metrics.txt

To reproduce the search results table

sh scripts/ search experiments/search.yaml

python scripts/ search
python scripts/ search/summary.txt

To run the training for an individual model instead of the entire set of 30 random restarts, run one of the following scripts


Each script takes 3 arguments: the yaml config file (e.g. see experiments/default.yaml or experiments/constrained.yaml), an output directory, and a random seed.

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