Deep neural models for core NLP tasks based on Pytorch(version 2)
This is the code we used in the following papers
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
Xuezhe Ma, Eduard Hovy
ACL 2016
Neural Probabilistic Model for Non-projective MST Parsing
Xuezhe Ma, Eduard Hovy
IJCNLP 2017
Stack-Pointer Networks for Dependency Parsing
Xuezhe Ma, Zecong Hu, Jingzhou Liu, Nanyun Peng, Graham Neubig and Eduard Hovy
ACL 2018
It also includes the re-implementation of the Stanford Deep BiAffine Parser:
Deep Biaffine Attention for Neural Dependency Parsing
Timothy Dozat, Christopher D. Manning
ICLR 2017
Python 3.6, PyTorch >=1.3.1, Gensim >= 0.12.0
For the data format used in our implementation, please read this issue.
First to the experiments folder:
cd experiments
To train a CRF POS tagger of PTB WSJ corpus,
./scripts/run_pos_wsj.sh
where the arguments for train/dev/test
data, together with the pretrained word embedding should be setup.
To train a NER model on CoNLL-2003 English data set,
./scripts/run_ner_conll03.sh
To train a Stack-Pointer parser, simply run
./scripts/run_stackptr.sh
Remeber to setup the paths for data and embeddings.
To train a Deep BiAffine parser, simply run
./scripts/run_deepbiaf.sh
Again, remember to setup the paths for data and embeddings.
To train a Neural MST parser,
./scripts/run_neuromst.sh