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  • John Snow Labs
  • Apr 06, 2023

Legal NER for NDA (Confidential Information-Permissions)

  • en
  • licensed
  • legal
  • ner
  • nda
  • permission

Description

This is a NER model, aimed to be run only after detecting the USE_OF_CONF_INFO clause with a proper classifier (use legmulticlf_mnda_sections_paragraph_other for that purpose). It will extract the following entities: PERMISSION, PERMISSION_SUBJECT , PERMISSION_OBJECT , and PERMISSION_IND_OBJECT .

Predicted Entities

PERMISSION, PERMISSION_SUBJECT, PERMISSION_OBJECT, PERMISSION_IND_OBJECT

Download Copy S3 URI

How to use

document_assembler = nlp.DocumentAssembler()\
        .setInputCol("text")\
        .setOutputCol("document")
        
sentence_detector = nlp.SentenceDetector()\
        .setInputCols(["document"])\
        .setOutputCol("sentence")

tokenizer = nlp.Tokenizer()\
        .setInputCols(["sentence"])\
        .setOutputCol("token")

embeddings = nlp.RoBertaEmbeddings.pretrained("roberta_embeddings_legal_roberta_base","en") \
        .setInputCols(["sentence", "token"]) \
        .setOutputCol("embeddings")\
        .setMaxSentenceLength(512)\
        .setCaseSensitive(True)

ner_model = legal.NerModel.pretrained("legner_nda_confidential_information_permissions", "en", "legal/models")\
        .setInputCols(["sentence", "token", "embeddings"])\
        .setOutputCol("ner")

ner_converter = nlp.NerConverter()\
        .setInputCols(["sentence", "token", "ner"])\
        .setOutputCol("ner_chunk")

nlpPipeline = nlp.Pipeline(stages=[
        document_assembler,
        sentence_detector,
        tokenizer,
        embeddings,
        ner_model,
        ner_converter])

empty_data = spark.createDataFrame([[""]]).toDF("text")

model = nlpPipeline.fit(empty_data)

text = ["""The interested party may disclose the information to its financing sources and potential financing sources provided that such financing sources are bound by the terms of this non-disclosure agreement and agree to keep the information confidential."""]

result = model.transform(spark.createDataFrame([text]).toDF("text"))

Results

+---------------------------+---------------------+
|chunk                      |ner_label            |
+---------------------------+---------------------+
|interested party           |PERMISSION_SUBJECT   |
|disclose                   |PERMISSION           |
|information                |PERMISSION_OBJECT    |
|financing sources          |PERMISSION_IND_OBJECT|
|potential financing sources|PERMISSION_IND_OBJECT|
+---------------------------+---------------------+

Model Information

Model Name: legner_nda_confidential_information_permissions
Compatibility: Legal NLP 1.0.0+
License: Licensed
Edition: Official
Input Labels: [sentence, token, embeddings]
Output Labels: [ner]
Language: en
Size: 16.3 MB

References

In-house annotations on the Non-disclosure Agreements

Benchmarking

label                  precision  recall  f1-score  support 
PERMISSION             1.00       1.00    1.00      9       
PERMISSION_IND_OBJECT  1.00       0.67    0.80      9       
PERMISSION_OBJECT      0.91       1.00    0.95      10      
PERMISSION_SUBJECT     0.90       1.00    0.95      9       
micro-avg              0.94       0.92    0.93      37      
macro-avg              0.95       0.92    0.92      37      
weighted-avg           0.95       0.92    0.93      37