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  • John Snow Labs
  • Nov 09, 2022

Legal Relation Extraction (Confidentiality, md, Unidirectional)

  • en
  • legal
  • licensed
  • confidentiality
  • re

Description

IMPORTANT: Don’t run this model on the whole legal agreement. Instead:

  • Split by paragraphs. You can use notebook 1 in Finance or Legal as inspiration;
  • Use the legclf_cuad_confidentiality_clause Text Classifier to select only these paragraphs;

This is a Legal Relation Extraction Model to identify the Subject (who), Action (what), Object(the confidentiality) and Indirect Object (to whom) from confidentiality clauses. This model requires legner_confidentiality as an NER in the pipeline. It’s a md model with Unidirectional Relations, meaning that the model retrieves in chunk1 the left side of the relation (source), and in chunk2 the right side (target).

Predicted Entities

is_confidentiality_indobject, is_confidentiality_object, is_confidentiality_subject

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How to use


documentAssembler = nlp.DocumentAssembler()\
  .setInputCol("text")\
  .setOutputCol("document")

sentencizer = nlp.SentenceDetectorDLModel\
        .pretrained("sentence_detector_dl", "en") \
        .setInputCols(["document"])\
        .setOutputCol("sentence")

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

pos_tagger = nlp.PerceptronModel()\
    .pretrained() \
    .setInputCols(["sentence", "token"])\
    .setOutputCol("pos_tags")

dependency_parser = nlp.DependencyParserModel() \
    .pretrained("dependency_conllu", "en") \
    .setInputCols(["sentence", "pos_tags", "token"]) \
    .setOutputCol("dependencies")

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

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

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

re_filter = legal.RENerChunksFilter()\
    .setInputCols(["ner_chunk", "dependencies"])\
    .setOutputCol("re_ner_chunks")\
    .setMaxSyntacticDistance(10)\
    .setRelationPairs(['CONFIDENTIALITY_ACTION-CONFIDENTIALITY_SUBJECT','CONFIDENTIALITY_ACTION-CONFIDENTIALITY','CONFIDENTIALITY_SUBJECT-CONFIDENTIALITY_INDIRECT_OBJECT'])

reDL = legal.RelationExtractionDLModel.pretrained("legre_confidentiality_md", "en", "legal/models") \
    .setPredictionThreshold(0.5) \
    .setInputCols(["re_ner_chunks", "sentence"]) \
    .setOutputCol("relations")
    
pipeline = nlp.Pipeline(stages=[documentAssembler,sentencizer, tokenizer,pos_tagger,dependency_parser, embeddings, ner_model, ner_converter,re_filter, reDL])

text = """Each party acknowledges that the other's Confidential Information contains valuable trade secret  and proprietary information of that party."""

data = spark.createDataFrame([[text]]).toDF("text")
model = pipeline.fit(data)
res = model.transform(data)

Results

+--------------------------+----------------------+-------------+-----------+------------+-----------------------+-------------+-----------+-----------------------------------------+----------+
|relation                  |entity1               |entity1_begin|entity1_end|chunk1      |entity2                |entity2_begin|entity2_end|chunk2                                   |confidence|
+--------------------------+----------------------+-------------+-----------+------------+-----------------------+-------------+-----------+-----------------------------------------+----------+
|is_confidentiality_subject|CONFIDENTIALITY_ACTION|11           |22         |acknowledges|CONFIDENTIALITY_SUBJECT|0            |9          |Each party                               |0.67629266|
|is_confidentiality_object |CONFIDENTIALITY_ACTION|11           |22         |acknowledges|CONFIDENTIALITY        |41           |64         |Confidential Information                 |0.99151576|
|is_confidentiality_object |CONFIDENTIALITY_ACTION|11           |22         |acknowledges|CONFIDENTIALITY        |84           |124        |trade secret  and proprietary information|0.98372066|
+--------------------------+----------------------+-------------+-----------+------------+-----------------------+-------------+-----------+-----------------------------------------+----------+

Model Information

Model Name: legre_confidentiality_md
Compatibility: Legal NLP 1.0.0+
License: Licensed
Edition: Official
Language: en
Size: 402.3 MB

References

Manual annotations on CUAD dataset

Benchmarking

                       label     Recall Precision        F1   Support 
is_confidentiality_indobject      0.960     1.000     0.980        25 
   is_confidentiality_object      1.000     0.933     0.966        56
  is_confidentiality_subject      0.935     1.000     0.967        31
                       other      0.989     1.000     0.994        88
                         Avg      0.971     0.983     0.977        -
                Weighted-Avg      0.980     0.981     0.980        -