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  • John Snow Labs
  • Aug 16, 2022

Legal NER (Parties, Dates, Document Type - sm)

  • en
  • legal
  • ner
  • agreements
  • licensed

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_introduction_clause Text Classifier to select only these paragraphs;

This is a Legal NER Model, aimed to process the first page of the agreements when information can be found about:

  • Parties of the contract/agreement;
  • Aliases of those parties, or how those parties will be called further on in the document;
  • Document Type;
  • Effective Date of the agreement;

This model can be used all along with its Relation Extraction model to retrieve the relations between these entities, called legre_contract_doc_parties

Other models can be found to detect other parts of the document, as Headers/Subheaders, Signers, “Will-do”, etc.

Predicted Entities

PARTY, EFFDATE, DOC, ALIAS

Live Demo Copy S3 URI

How to use

documentAssembler = nlp.DocumentAssembler()\
        .setInputCol("text")\
        .setOutputCol("document")
        
sentenceDetector = nlp.SentenceDetectorDLModel.pretrained("sentence_detector_dl","xx")\
        .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")\

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

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

nlpPipeline = nlp.Pipeline(stages=[
        documentAssembler,
        sentenceDetector,
        tokenizer,
        embeddings,
        ner_model,
        ner_converter])

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

model = nlpPipeline.fit(empty_data)

text = ["""
INTELLECTUAL PROPERTY AGREEMENT

This INTELLECTUAL PROPERTY AGREEMENT (this "Agreement"), dated as of December 31, 2018 (the "Effective Date") is entered into by and between Armstrong Flooring, Inc., a Delaware corporation ("Seller") and AFI Licensing LLC, a Delaware limited liability company ("Licensing" and together with Seller, "Arizona") and AHF Holding, Inc. (formerly known as Tarzan HoldCo, Inc.), a Delaware corporation ("Buyer") and Armstrong Hardwood Flooring Company, a Tennessee corporation (the "Company" and together with Buyer the "Buyer Entities") (each of Arizona on the one hand and the Buyer Entities on the other hand, a "Party" and collectively, the "Parties").
"""]

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

Results

+------------+---------+
|       token|ner_label|
+------------+---------+
|INTELLECTUAL|    B-DOC|
|    PROPERTY|    I-DOC|
|   AGREEMENT|    I-DOC|
|        This|        O|
|INTELLECTUAL|    B-DOC|
|    PROPERTY|    I-DOC|
|   AGREEMENT|    I-DOC|
|           (|        O|
|        this|        O|
|           "|        O|
|   Agreement|        O|
|         "),|        O|
|       dated|        O|
|          as|        O|
|          of|        O|
|    December|B-EFFDATE|
|          31|I-EFFDATE|
|           ,|I-EFFDATE|
|        2018|I-EFFDATE|
|           (|        O|
|         the|        O|
|           "|        O|
|   Effective|        O|
|        Date|        O|
|          ")|        O|
|          is|        O|
|     entered|        O|
|        into|        O|
|          by|        O|
|         and|        O|
|     between|        O|
|   Armstrong|  B-PARTY|
|    Flooring|  I-PARTY|
|           ,|  I-PARTY|
|         Inc|  I-PARTY|
|          .,|        O|
|           a|        O|
|    Delaware|        O|
| corporation|        O|
|          ("|        O|
|      Seller|  B-ALIAS|
|          ")|        O|
|         and|        O|
|         AFI|  B-PARTY|
|   Licensing|  I-PARTY|
|         LLC|  I-PARTY|
|           ,|        O|
|           a|        O|
|    Delaware|        O|
|     limited|        O|
|   liability|        O|
|     company|        O|
|          ("|        O|
|   Licensing|  B-ALIAS|
|           "|        O|
|         and|        O|
|    together|        O|
|        with|        O|
|      Seller|  B-ALIAS|
|           ,|        O|
|           "|        O|
|     Arizona|  B-ALIAS|
|          ")|        O|
|         and|        O|
|         AHF|  B-PARTY|
|     Holding|  I-PARTY|
|           ,|  I-PARTY|
|         Inc|  I-PARTY|
|           .|        O|
|           (|        O|
|    formerly|        O|
|       known|        O|
|          as|        O|
|      Tarzan|        O|
|      HoldCo|        O|
|           ,|        O|
|         Inc|        O|
|         .),|        O|
|           a|        O|
|    Delaware|        O|
| corporation|        O|
|          ("|        O|
|       Buyer|  B-ALIAS|
|          ")|        O|
|         and|        O|
|   Armstrong|  B-PARTY|
|    Hardwood|  I-PARTY|
|    Flooring|  I-PARTY|
|     Company|  I-PARTY|
------------------------

Model Information

Model Name: legner_contract_doc_parties
Type: legal
Compatibility: Legal NLP 1.0.0+
License: Licensed
Edition: Official
Input Labels: [sentence, token, embeddings]
Output Labels: [ner]
Language: en
Size: 16.5 MB

References

Manual annotations on CUAD dataset

Benchmarking

label       tp     fp    fn    prec          rec           f1
I-PARTY     262    20    61    0.92907804    0.8111455     0.8661157
B-EFFDATE   22     4     9     0.84615386    0.7096774     0.77192986
B-DOC       38     4     12    0.9047619     0.76          0.82608694
I-EFFDATE   95     9     19    0.91346157    0.8333333     0.8715596
I-DOC       93     12    5     0.8857143     0.9489796     0.9162561
B-PARTY     88     10    29    0.8979592     0.75213677    0.81860465
B-ALIAS     64     7     14    0.90140843    0.82051283    0.8590604