Implementations of joint models for POS tagging and dependency parsing, as described in my papers:

1. Dat Quoc Nguyen and Karin Verspoor. 2018. An improved neural network model for joint POS tagging and dependency parsing. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 81-91. [.bib] (jPTDP v2.0)
2. Dat Quoc Nguyen, Mark Dras and Mark Johnson. 2017. A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 134-142. [.bib] (jPTDP v1.0)

This github project currently supports jPTDP v2.0, while v1.0 can be found in the release section. Please cite paper [1] when jPTDP is used to produce published results or incorporated into other software. I would highly appreciate to have your bug reports, comments and suggestions about jPTDP. As a free open-source implementation, jPTDP is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

jPTDP requires the following software packages:

• Python 2.7

• DyNet v2.0

  $ virtualenv -p python2.7 .DyNet
  $ source .DyNet/bin/activate
  $ pip install cython numpy
  $ pip install dynet==2.0.3
  

Once you installed the prerequisite packages above, you can clone or download (and then unzip) jPTDP. Next sections show instructions to train a new joint model for POS tagging and dependency parsing, and then to utilize a pre-trained model.

Suppose that SOURCE_DIR is simply used to denote the source code directory. Similar to files train.conllu and dev.conllu in folder SOURCE_DIR/sample or treebanks in the Universal Dependencies (UD) project, the training and development files are formatted following 10-column data format. For training, jPTDP will only use information from columns 1 (ID), 2 (FORM), 4 (Coarse-grained POS tags---UPOSTAG), 7 (HEAD) and 8 (DEPREL).

To train a joint model for POS tagging and dependency parsing, you perform:

SOURCE_DIR$ python jPTDP.py --dynet-seed 123456789 [--dynet-mem <int>] [--epochs <int>] [--lstmdims <int>] [--lstmlayers <int>] [--hidden <int>] [--wembedding <int>] [--cembedding <int>] [--pembedding <int>] [--prevectors <path-to-pre-trained-word-embedding-file>] [--model <String>] [--params <String>] --outdir <path-to-output-directory> --train <path-to-train-file>  --dev <path-to-dev-file>

where hyper-parameters in [] are optional:

• --dynet-mem: Specify DyNet memory in MB.
• --epochs: Specify number of training epochs. Default value is 30.
• --lstmdims: Specify number of BiLSTM dimensions. Default value is 128.
• --lstmlayers: Specify number of BiLSTM layers. Default value is 2.
• --hidden: Specify size of MLP hidden layer. Default value is 100.
• --wembedding: Specify size of word embeddings. Default value is 100.
• --cembedding: Specify size of character embeddings. Default value is 50.
• --pembedding: Specify size of POS tag embeddings. Default value is 100.
• --prevectors: Specify path to the pre-trained word embedding file for initialization. Default value is "None" (i.e. word embeddings are randomly initialized).
• --model: Specify a name for model parameters file. Default value is "model".
• --params: Specify a name for model hyper-parameters file. Default value is "model.params".
• --outdir: Specify path to directory where the trained model will be saved.
• --train: Specify path to the training data file.
• --dev: Specify path to the development data file.

For example:

SOURCE_DIR$ python jPTDP.py --dynet-seed 123456789 --dynet-mem 1000 --epochs 30 --lstmdims 128 --lstmlayers 2 --hidden 100 --wembedding 100 --cembedding 50 --pembedding 100  --model trialmodel --params trialmodel.params --outdir sample/ --train sample/train.conllu --dev sample/dev.conllu

will produce model files trialmodel and trialmodel.params in folder SOURCE_DIR/sample.

If you would like to use the fine-grained language-specific POS tags in the 5th column instead of the coarse-grained POS tags in the 4th column, you should use swapper.py in folder SOURCE_DIR/utils to swap contents in the 4th and 5th columns:

SOURCE_DIR$ python utils/swapper.py <path-to-train-(and-dev)-file>

For example:

SOURCE_DIR$ python utils/swapper.py sample/train.conllu
SOURCE_DIR$ python utils/swapper.py sample/dev.conllu

will generate two new files for training: train.conllu.ux2xu and dev.conllu.ux2xu in folder SOURCE_DIR/sample.

Assume that you are going to utilize a pre-trained model for annotating a corpus whose each line represents a tokenized/word-segmented sentence. You should use converter.py in folder SOURCE_DIR/utils to obtain a 10-column data format of this corpus:

SOURCE_DIR$ python utils/converter.py <file-path>

For example:

SOURCE_DIR$ python utils/converter.py sample/test

will generate in folder SOURCE_DIR/sample a file named test.conllu which can be used later as input to the pre-trained model.

To utilize a pre-trained model for POS tagging and dependency parsing, you perform:

SOURCE_DIR$ python jPTDP.py --predict --model <path-to-model-parameters-file> --params <path-to-model-hyper-parameters-file> --test <path-to-10-column-input-file> --outdir <path-to-output-directory> --output <String>

• --model: Specify path to model parameters file.
• --params: Specify path to model hyper-parameters file.
• --test: Specify path to 10-column input file.
• --outdir: Specify path to directory where output file will be saved.
• --output: Specify name of the output file.

For example:

SOURCE_DIR$ python jPTDP.py --predict --model sample/trialmodel --params sample/trialmodel.params --test sample/test.conllu --outdir sample/ --output test.conllu.pred
SOURCE_DIR$ python jPTDP.py --predict --model sample/trialmodel --params sample/trialmodel.params --test sample/dev.conllu --outdir sample/ --output dev.conllu.pred

will produce output files test.conllu.pred and dev.conllu.pred in folder SOURCE_DIR/sample.

Pre-trained jPTDP v2.0 models, which were trained on English WSJ Penn treebank, GENIA and UD v2.2 treebanks, can be found at HERE. Results on test sets (as detailed in paper [1]) are as follows:

model256 and model denote the pre-trained models which use 256- and 128-dimensional LSTM hidden states, respectively, i.e. model256 is more accurate but slower.