Polish morphological tagger.
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
krnnt
Readme.md
create_simple_lemmatization.py
krnnt_run.py
krnnt_serve.py
krnnt_train.py
merge_analyzed_gold.py
process_xces.py
reanalyze.py
requirements.txt
setup.py
shuffle.py
voting.py

Readme.md

KRNNT

KRNNT is a morphological tagger for Polish based on recurrent naural networks. It was presented at 8th Language & Technology Conference. More details are available in the paper:

@inproceedings{salam,
  author       = "Wróbel, Krzysztof",
  editor       = "Vetulani, Zygmunt and Paroubek, Patrick",
  title        = "KRNNT: Polish Recurrent Neural Network Tagger",
  year         = "2017",
  booktitle    = "Proceedings of the 8th Language \& Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics",
  publisher    = "Fundacja Uniwersytetu im. Adama Mickiewicza w~Poznaniu",
  pages        = {386-391},
  pdf          = "http://ltc.amu.edu.pl/book/papers/PolEval1-6.pdf"
}

Online version is available at: http://ltc.amu.edu.pl/book/papers/PolEval1-6.pdf

External tools

The tagger uses external tools: tokenizer Toki and morphological analyzer Morfeusz. Maca (http://nlp.pwr.wroc.pl/redmine/projects/libpltagger/wiki) integrates both tools.

The tagset is described here: http://nkjp.pl/poliqarp/help/ense2.html

Getting started

You can run KRNNT using docker or by manual installation.

Docker

Docker image was prepared by Aleksander Smywiński-Pohl and instrutions are available at: https://hub.docker.com/r/djstrong/krnnt/

  1. Download and starte the server.
docker run -it -p 9200:9200 djstrong/krnnt:0.1 python3 /home/krnnt/krnnt/krnnt_serve.py /home/krnnt/krnnt/data
  1. Tag a text usig POST request or open http://localhost:9200 in a browser.
curl -XPOST localhost:9200 -d "Ala ma kota."
Ala    none
    Ala    subst:sg:nom:f    disamb
ma    space
    mieć    fin:sg:ter:imperf    disamb
kota    space
    kot    subst:sg:acc:m2    disamb
.    none
    .    interp    disamb

Manual installation

  1. Install Maca: http://nlp.pwr.wroc.pl/redmine/projects/libpltagger/wiki

Make sure that command maca-analyse works:

echo "Ala ma kota." | maca-analyse -qc morfeusz-nkjp-official
Ala	newline
	Al	subst:sg:gen:m1
	Al	subst:sg:acc:m1
	Ala	subst:sg:nom:f
	Alo	subst:sg:gen:m1
	Alo	subst:sg:acc:m1
ma	space
	mieć	fin:sg:ter:imperf
	mój	adj:sg:nom:f:pos
	mój	adj:sg:voc:f:pos
kota	space
	Kot	subst:sg:gen:m1
	Kot	subst:sg:acc:m1
	kot	subst:sg:gen:m1
	kot	subst:sg:acc:m1
	kot	subst:sg:gen:m2
	kot	subst:sg:acc:m2
	kota	subst:sg:nom:f
.	none
	.	interp
  1. Clone KRNNT repository:
git clone https://github.com/kwrobel-nlp/krnnt.git
  1. Install dependencies.
pip3 install -e .

Evaluation

Accuracy tested with 10-fold cross validation on National Corpus of Polish.

Accuracy lower bound Accuracy lower bound for unknown tokens
93.72% 69.03%

PolEval

The tagger particaipated in PolEval 2017 competition: http://poleval.pl/

There is some problem with Keras version higher than 2.1.2.

Training

  1. Install KRNNT.
krnnt]$ pip3 install -e .
  1. Prepare training data.
krnnt]$ time python3 process_xces.py train-gold.xml train-gold.spickle
real	0m37.769s
  1. Reanalyze corpus with Maca.
krnnt]$ python3 reanalyze.py train-gold.spickle train-reanalyzed.spickle
0 MACA 9 10
1 MACA 7 8
...
real	26m35.013s

Ensure that last two numbers in each row are usually the same. Zeros indicates problems with Maca.

  1. Shuffle data (optional).
krnnt]$ time python3 shuffle.py train-reanalyzed.spickle train-reanalyzed.shuf.spickle
real	1m26.350s
  1. Train for 100 epochs. Add -d 0.1 for using 10% of training data as development data set.
krnnt]$ time python3 krnnt_train.py train-reanalyzed.shuf.spickle --patience 100
Model is saved under: weight_1810e860-6351-11e7-ae0b-a0000220fe80.hdf5.final
Lemmatisation model is saved under: lemmatisation_1810e860-6351-11e7-ae0b-a0000220fe80.pkl
Dictionary is saved under: train-reanalyzed.shuf.spickle_FormatData2_PreprocessData_UniqueFeaturesValues
real    197m44,568s

Check ~/.keras/keras.json for "image_dim_ordering": "th" (for old Keras) and "image_data_format": "channels_first" (for Keras 2).

  1. Testing.
krnnt]$ time python3 krnnt_run.py weight_1810e860-6351-11e7-ae0b-a0000220fe80.hdf5.final lemmatisation_1810e860-6351-11e7-ae0b-a0000220fe80.pkl train-reanalyzed.shuf.spickle_FormatData2_PreprocessData_UniqueFeaturesValues < train-raw.txt
real	7m22.892s

Training on gold segmentation

  1. Prepare training data.
krnnt]$ time python3 process_xces.py train-analyzed.xml train-analyzed.spickle
real	1m51.836s

krnnt]$ time python3 process_xces.py train-gold.xml train-gold.spickle
real	0m37.769s

krnnt]$ time python3 merge_analyzed_gold.py train-gold.spickle train-analyzed.spickle train-merged.spickle
real	0m36.049s
  1. Shuffle data (optional).
krnnt]$ time python3 shuffle.py train-merged.spickle train-merged.shuf.spickle
real	1m41.192s
  1. Train for 100 epochs.
krnnt]$ time python3 krnnt_train.py -p train-merged.shuf.spickle --patience 100
Model is saved under: weight_1810e860-6351-11e7-ae0b-a0000220fe80.hdf5.final
Lemmatisation model is saved under: lemmatisation_1810e860-6351-11e7-ae0b-a0000220fe80.pkl
Dictionary is saved under: train-reanalyzed.shuf.spickle_FormatData2_PreprocessData_UniqueFeaturesValues
real    190m44,568s
  1. Testing.
krnnt]$ time python3 krnnt_run.py -p weight_1810e860-6351-11e7-ae0b-a0000220fe80.hdf5.final lemmatisation_1810e860-6351-11e7-ae0b-a0000220fe80.pkl train-reanalyzed.shuf.spickle_FormatData2_PreprocessData_UniqueFeaturesValues < train-analyzed.xces
real	7m38.660s

Testing

Trained models are available with releases: https://github.com/kwrobel-nlp/krnnt/releases

krnnt]$ pip3 install -e .

reana.zip contains model trained with reanalyzed data:

krnnt]$ python3 krnnt_run.py reana/weights_reana.hdf5 reana/lemmatisation_reana.pkl reana/dictionary_reana.pkl < test-raw.txt > test-raw.krnnt.xml

preana.zip contains model trained with preanalyzed data:

krnnt]$ python3 krnnt_run.py -p preana/weights_preana.hdf5 preana/lemmatisation_preana.pkl preana/dictionary_preana.pkl < test-analyzed.xml > test-analyzed.krnnt.xml

Voting

Training more models and performing simple voting increase accuracy. Voting over 10 models achieves about 94.30% accuracy lower bound.

reana10.zip and preana10.zip contain 10 models each.

for i in {0..9}
do
   krnnt]$ python3 krnnt_run.py reana/$i.hdf5 reana/lemmatisation.pkl  reana/dictionary.pkl < test-raw.txt > reana/$i.xml
done
krnnt]$ python3 voting.py reana/ > reana/test-raw.krnnt.voting.xml

KRNNT is licensed under GNU LGPL v3.0.