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README.md

CRF autoencoder for unsupervised dependency parsing

This repository contains the code to reproduce the experiment result of the paper CRF autoencoder for unsupervised dependency parsing on WSJ data set and PASCAL dataset.

Prerequisites

  1. Java 8
  2. sbt 0.13.13

Compile

sbt assembly

Example Usage

  1. Experiments on WSJ dataset.
java -cp target/scala-2.12/CRFAE-Dep-Parser-assembly-1.0.jar edu.shanghaitech.nlp.crfae.parser.DepParserLauncher --train-file wsj10-train.txt --test-file wsj-test.txt --model-type projective --training-type hard --reg-type L1 --rules-type wsj --km-type decoder --gd-num-passes 10 --em-num-passes 10 --batch-size 200 --init-rate 0.1 --lambda 2.5 --prior-weight 9.0
  1. Experiments on Pascal dataset.
java -cp target/scala-2.12/CRFAE-Dep-Parser-assembly-1.0.jar edu.shanghaitech.nlp.crfae.parser.DepParserLauncher --train-file pascal-{}-train.txt --test-file pascal-{}-test.txt --model-type projective --training-type hard --reg-type L1 --rules-type ud --km-type joint --gd-num-passes 2 --em-num-passes 2 --batch-size 200 --init-rate {} --lambda {} --prior-weight {}
language learning rate (init-rate) lambda prior weight
basque 0.01 2.5 0.1
czech 0.01 1.0 1.0
danish 0.05 2.5 25.0
dutch 0.1 2.5 9.0
protuguese 0.05 2.5 5.0
slovene 0.01 0.5 1.0
swedish 0.05 0.5 25.0

Input format

No	wonder	
DT	NN	
DT	NN	
2	0	

So	he	adjusts	
RB	PRP	VBZ	
RB	PRP	VBZ	
3	3	0	
  1. Sequence of words.
  2. Sequence of POS.
  3. Sequence of Universal POS. (If it is not available, duplicate the sequence of POS.)
  4. Sequence of Head index.

Citation

If you found this repository helpful, you could cite

@inproceedings{cai2017crf,
  title={CRF Autoencoder for Unsupervised Dependency Parsing},
  author={Cai, Jiong and Jiang, Yong and Tu, Kewei},
  booktitle={Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  pages={1639--1644},
  year={2017}
}

Licence

This code is distributed under GPLv3.0 LICENSE.