NER for German Legal Text in Flair (default model)
This is the legal NER model for German that ships with Flair.
F1-Score: 96,35 (LER German dataset)
Predicts 19 tags:
tag | meaning |
---|---|
AN | Anwalt |
EUN | Europäische Norm |
GS | Gesetz |
GRT | Gericht |
INN | Institution |
LD | Land |
LDS | Landschaft |
LIT | Literatur |
MRK | Marke |
ORG | Organisation |
PER | Person |
RR | Richter |
RS | Rechtssprechung |
ST | Stadt |
STR | Straße |
UN | Unternehmen |
VO | Verordnung |
VS | Vorschrift |
VT | Vertrag |
Based on Flair embeddings and LSTM-CRF.
More details on the Legal NER dataset here
Demo: How to use in Flair
Requires: Flair (pip install flair
)
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-german-legal")
# make example sentence (don't use tokenizer since Rechtstexte are badly handled)
sentence = Sentence("Herr W. verstieß gegen § 36 Abs. 7 IfSG.", use_tokenizer=False)
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
This yields the following output:
Span [2]: "W." [− Labels: PER (0.9911)]
Span [5,6,7,8,9]: "§ 36 Abs. 7 IfSG." [− Labels: GS (0.5353)]
So, the entities "W." (labeled as a person) and "§ 36 Abs. 7 IfSG" (labeled as a Gesetz) are found in the sentence "Herr W. verstieß gegen § 36 Abs. 7 IfSG.".
Training: Script to train this model
The following Flair script was used to train this model:
from flair.data import Corpus
from flair.datasets import LER_GERMAN
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. get the corpus
corpus: Corpus = LER_GERMAN()
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize each embedding we use
embedding_types = [
# GloVe embeddings
WordEmbeddings('de'),
# contextual string embeddings, forward
FlairEmbeddings('de-forward'),
# contextual string embeddings, backward
FlairEmbeddings('de-backward'),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/ner-german-legal',
train_with_dev=True,
max_epochs=150)
Cite
Please cite the following papers when using this model.
@inproceedings{leitner2019fine,
author = {Elena Leitner and Georg Rehm and Julian Moreno-Schneider},
title = {{Fine-grained Named Entity Recognition in Legal Documents}},
booktitle = {Semantic Systems. The Power of AI and Knowledge
Graphs. Proceedings of the 15th International Conference
(SEMANTiCS 2019)},
year = 2019,
pages = {272--287},
pdf = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-33220-4_20.pdf}}
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
Issues?
The Flair issue tracker is available here.
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