deepmultilingualpunctuation 1.0.1

A python package for deep multilingual punctuation prediction.

Deep Multilingual Punctuation Prediction

This python library predicts the punctuation of English, Italian, French and German texts. We developed it to restore the punctuation of transcribed spoken language.

This uses our "FullStop" model that we trained on the Europarl Dataset. Please note that this dataset consists of political speeches. Therefore the model might perform differently on texts from other domains.

The code restores the following punctuation markers: "." "," "?" "-" ":"

Video Sample

Install

To get started install the package from pypi:

pip install deepmultilingualpunctuation

Usage

The PunctuationModel class an process texts of any length. Note that processing of very long texts can be time consuming.

Restore Punctuation

from deepmultilingualpunctuation import PunctuationModel

model = PunctuationModel()
text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller"
result = model.restore_punctuation(text)
print(result)

output

My name is Clara and I live in Berkeley, California. Ist das eine Frage, Frau Müller?

Predict Labels

from deepmultilingualpunctuation import PunctuationModel

model = PunctuationModel()
text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller"
clean_text = model.preprocess(text)
labled_words = model.predict(clean_text)
print(labled_words)

output

[['My', '0', 0.9999887], ['name', '0', 0.99998665], ['is', '0', 0.9998579], ['Clara', '0', 0.6752215], ['and', '0', 0.99990904], ['I', '0', 0.9999877], ['live', '0', 0.9999839], ['in', '0', 0.9999515], ['Berkeley', ',', 0.99800044], ['California', '.', 0.99534047], ['Ist', '0', 0.99998784], ['das', '0', 0.99999154], ['eine', '0', 0.9999918], ['Frage', ',', 0.99622655], ['Frau', '0', 0.9999889], ['Müller', '?', 0.99863917]]

Results

The performance differs for the single punctuation markers as hyphens and colons, in many cases, are optional and can be substituted by either a comma or a full stop. The model achieves the following F1 scores for the different languages:

Label	EN	DE	FR	IT
0	0.991	0.997	0.992	0.989
.	0.948	0.961	0.945	0.942
?	0.890	0.893	0.871	0.832
,	0.819	0.945	0.831	0.798
:	0.575	0.652	0.620	0.588
-	0.425	0.435	0.431	0.421
macro average	0.775	0.814	0.782	0.762

References

Please cite us if you found this useful:

@article{guhr-EtAl:2021:fullstop,
  title={FullStop: Multilingual Deep Models for Punctuation Prediction},
  author    = {Guhr, Oliver  and  Schumann, Anne-Kathrin  and  Bahrmann, Frank  and  Böhme, Hans Joachim},
  booktitle      = {Proceedings of the Swiss Text Analytics Conference 2021},
  month          = {June},
  year           = {2021},
  address        = {Winterthur, Switzerland},
  publisher      = {CEUR Workshop Proceedings},  
  url       = {http://ceur-ws.org/Vol-2957/sepp_paper4.pdf}
}