Description
This model can be used to detect legal entities in German text, predicting up to 19 different labels:
| 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
German Named Entity Recognition model, trained using a Deep Learning architecture (CharCNN + LSTM) with a Court Decisions (2017-2018) dataset (check Data Source section). You can also find transformer-based versions of this model in our Models Hub (legner_bert_base_courts and legner_bert_large_courts)
Predicted Entities
STR, LIT, PER, EUN, VT, MRK, INN, UN, RS, ORG, GS, VS, LDS, GRT, VO, RR, LD, AN, ST
How to use
...
word_embeddings = nlp.WordEmbeddingsModel.pretrained("w2v_cc_300d",'de','clinical/models')\
.setInputCols(["sentence", 'token'])\
.setOutputCol("embeddings")\
.setCaseSensitive(False)
legal_ner = legal.NerModel.pretrained("legner_courts",'de','legal/models') \
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("ner")
...
legal_pred_pipeline = nlp.Pipeline(stages = [document_assembler, sentence_detector, tokenizer, word_embeddings, legal_ner, ner_converter])
legal_light_model = LightPipeline(legal_pred_pipeline.fit(spark.createDataFrame([['']]).toDF("text")))
result = legal_light_model.fullAnnotate('''Jedoch wird der Verkehr darin naheliegend den Namen eines der bekanntesten Flüsse Deutschlands erkennen, welcher als Seitenfluss des Rheins durch Oberfranken, Unterfranken und Südhessen fließt und bei Mainz in den Rhein mündet. Klein , in : Maunz / Schmidt-Bleibtreu / Klein / Bethge , BVerfGG , § 19 Rn. 9 Richtlinien zur Bewertung des Grundvermögens – BewRGr – vom19. I September 1966 (BStBl I, S.890) ''')
Results
+---+---------------------------------------------------+----------+
| # | Chunks | Entities |
+---+---------------------------------------------------+----------+
| 0 | Deutschlands | LD |
+---+---------------------------------------------------+----------+
| 1 | Rheins | LDS |
+---+---------------------------------------------------+----------+
| 2 | Oberfranken | LDS |
+---+---------------------------------------------------+----------+
| 3 | Unterfranken | LDS |
+---+---------------------------------------------------+----------+
| 4 | Südhessen | LDS |
+---+---------------------------------------------------+----------+
| 5 | Mainz | ST |
+---+---------------------------------------------------+----------+
| 6 | Rhein | LDS |
+---+---------------------------------------------------+----------+
| 7 | Klein , in : Maunz / Schmidt-Bleibtreu / Klein... | LIT |
+---+---------------------------------------------------+----------+
| 8 | Richtlinien zur Bewertung des Grundvermögens –... | VS |
+---+---------------------------------------------------+----------+
| 9 | I September 1966 (BStBl I, S.890) | VS |
+---+---------------------------------------------------+----------+
Model Information
| Model Name: | legner_courts |
| Compatibility: | Legal NLP 1.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | de |
| Size: | 15.0 MB |
References
The dataset used to train this model is taken from Leitner, et.al (2019)
Leitner, E., Rehm, G., and Moreno-Schneider, J. (2019). Fine-grained Named Entity Recognition in Legal Documents. In Maribel Acosta, et al., editors, Semantic Systems. The Power of AI and Knowledge Graphs. Proceedings of the 15th International Conference (SEMANTiCS2019), number 11702 in Lecture Notes in Computer Science, pages 272–287, Karlsruhe, Germany, 9. Springer. 10/11 September 2019.
Source of the annotated text:
Court decisions from 2017 and 2018 were selected for the dataset, published online by the Federal Ministry of Justice and Consumer Protection. The documents originate from seven federal courts: Federal Labour Court (BAG), Federal Fiscal Court (BFH), Federal Court of Justice (BGH), Federal Patent Court (BPatG), Federal Social Court (BSG), Federal Constitutional Court (BVerfG) and Federal Administrative Court (BVerwG).
Benchmarking
label prec rec f1
Macro-average 0.9210195 0.9186192 0.9198177
Micro-average 0.9833763 0.9837547 0.9835655