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flair/
ner-german-legal

Name: ner-german-legal
Availability: InStock

flair

pytorch

token-classification

sequence-tagger-model

legal

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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)

python

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:

python

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?

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