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  • John Snow Labs
  • Oct 22, 2022

Augment Company Tickers with NASDAQ database

  • en
  • finance
  • companies
  • nasdaq
  • ticker
  • licensed

Description

This is a Financial Chunk Mapper, which will retrieve, given a normalized Company Name (using, for example, finer_nasdaq_data to obtain the official Nasdaq company name), extra information about the company, including:

  • Ticker
  • Stock Exchange
  • Section
  • Sic codes
  • Section
  • Industry
  • Category
  • Currency
  • Location
  • Previous names (first_name)
  • Company type (INC, CORP, etc)
  • and some more.

Predicted Entities

Live Demo Copy S3 URI

How to use


document_assembler = nlp.DocumentAssembler()\
      .setInputCol('text')\
      .setOutputCol('document')

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

embeddings = nlp.BertEmbeddings.pretrained("bert_embeddings_sec_bert_base","en") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("embeddings")

ner_model = finance.NerModel.pretrained("finner_ticker", "en", "finance/models")\
    .setInputCols(["document", "token", "embeddings"])\
    .setOutputCol("ner")

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

CM = finance.ChunkMapperModel.pretrained('finmapper_nasdaq_data_ticker', 'en', 'finance/models')\
      .setInputCols(["ner_chunk"])\
      .setOutputCol("mappings")\
      .setRel('company_name')

pipeline = Pipeline().setStages([document_assembler,
                                 tokenizer, 
                                 embeddings,
                                 ner_model, 
                                 ner_converter, 
                                 CM])
                                 
text = ["""There are some serious purchases and sales of GLE1 stock today."""]

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

model = pipeline.fit(test_data)

res= model.transform(test_data).select('mappings').collect()

Results

[Row(mappings=[Row(annotatorType='labeled_dependency', begin=46, end=49, result='AMZN', metadata={'sentence': '0', 'chunk': '0', 'entity': 'AMZN', 'relation': 'ticker', 'all_relations': ''}, embeddings=[]), Row(annotatorType='labeled_dependency', begin=46, end=49, result='Amazon.com Inc.', metadata={'sentence': '0', 'chunk': '0', 'entity': 'AMZN', 'relation': 'company_name', 'all_relations': ''}, embeddings=[]), Row(annotatorType='labeled_dependency', begin=46, end=49, result='Amazon.com', metadata={'sentence': '0', 'chunk': '0', 'entity': 'AMZN', 'relation': 'short_name', 'all_relations': ''}, embeddings=[]), Row(annotatorType='labeled_dependency', begin=46, end=49, result='Retail - Apparel & Specialty', metadata={'sentence': '0', 'chunk': '0', 'entity': 'AMZN', 'relation': 'industry', 'all_relations': ''}, embeddings=[]), Row(annotatorType='labeled_dependency', begin=46, end=49, result='Consumer Cyclical', metadata={'sentence': '0', 'chunk': '0', 'entity': 'AMZN', 'relation': 'sector', 'all_relations': ''}, embeddings=[]), Row(annotatorType='labeled_dependency', begin=57, end=61, result='NONE', metadata={'sentence': '0', 'chunk': '1', 'entity': 'today'}, embeddings=[])])]

Model Information

Model Name: finmapper_nasdaq_data_ticker
Compatibility: Finance NLP 1.0.0+
License: Licensed
Edition: Official
Input Labels: [ner_chunk]
Output Labels: [mappings]
Language: en
Size: 1.0 MB

References

NASDAQ Database