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
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
You can run KRNNT using docker or by manual installation.
Docker image was prepared by Aleksander Smywiński-Pohl and instrutions are available at: https://hub.docker.com/r/djstrong/krnnt/
- 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- 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- 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- Clone KRNNT repository:
git clone https://github.com/kwrobel-nlp/krnnt.git- Install dependencies.
pip3 install -e .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% |
The tagger particaipated in PolEval 2017 competition: http://poleval.pl/
There is some problem with Keras version higher than 2.1.2.
- Install KRNNT.
krnnt]$ pip3 install -e .
- Prepare training data.
krnnt]$ time python3 process_xces.py train-gold.xml train-gold.spickle
real 0m37.769s
- 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.
- Shuffle data (optional).
krnnt]$ time python3 shuffle.py train-reanalyzed.spickle train-reanalyzed.shuf.spickle
real 1m26.350s
- Train for 100 epochs. Add
-d 0.1for 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).
- 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
- 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
- Shuffle data (optional).
krnnt]$ time python3 shuffle.py train-merged.spickle train-merged.shuf.spickle
real 1m41.192s
- 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
- 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
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
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.
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