Description
This model can be used to extract the Ticker of Companies or Product names. A Ticker (stock symbol) is a unique series of letters assigned to a security for trading purposes. For example:
Company: Apple Inc.
Ticker: AAPL
Predicted Entities
has_ticker
How to use
document_assembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_detector = nlp.SentenceDetectorDLModel.pretrained("sentence_detector_dl","xx")\
.setInputCols(["document"])\
.setOutputCol("sentence")\
tokenizer = nlp.Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
embeddings = nlp.BertEmbeddings.pretrained("bert_embeddings_sec_bert_base","en") \
.setInputCols(["sentence", "token"]) \
.setOutputCol("embeddings")
ner_model_org = finance.NerModel.pretrained("finner_orgs_prods_alias","en","finance/models")\
.setInputCols(["sentence", "token", "embeddings"])\
.setOutputCol("ner_org")
ner_converter_org = nlp.NerConverter()\
.setInputCols(["sentence","token","ner_org"])\
.setOutputCol("ner_chunk_org")\
.setWhiteList(['ORG'])
ner_model_ticker = finance.NerModel.pretrained("finner_ticker", "en", "finance/models")\
.setInputCols(["sentence", "token", "embeddings"])\
.setOutputCol("ner_ticker")
ner_converter_ticker = nlp.NerConverter() \
.setInputCols(["sentence", "token", "ner_ticker"]) \
.setOutputCol("ner_chunk_ticker")
chunk_merger = finance.ChunkMergeApproach()\
.setInputCols("ner_chunk_ticker", "ner_chunk_org")\
.setOutputCol('ner_chunk')\
.setMergeOverlapping(True)
pos = nlp.PerceptronModel.pretrained("pos_anc", 'en')\
.setInputCols("sentence", "token")\
.setOutputCol("pos")
dependency_parser = nlp.DependencyParserModel().pretrained("dependency_conllu", "en")\
.setInputCols(["sentence", "pos", "token"])\
.setOutputCol("dependencies")
re_ner_chunk_filter = finance.RENerChunksFilter()\
.setInputCols(["ner_chunk", "dependencies"])\
.setOutputCol("re_ner_chunk")\
.setRelationPairs(["ORG-TICKER"])\
.setMaxSyntacticDistance(4)
re_Model = finance.RelationExtractionDLModel.pretrained("finre_has_ticker", "en", "finance/models")\
.setInputCols(["ner_chunk", "sentence"])\
.setOutputCol("relations")\
.setPredictionThreshold(0.2)
pipeline = nlp.Pipeline(stages=[
document_assembler,
sentence_detector,
tokenizer,
embeddings,
ner_model_org,
ner_converter_org,
ner_model_ticker,
ner_converter_ticker,
chunk_merger,
pos,
dependency_parser,
re_ner_chunk_filter,
re_Model])
empty_df = spark.createDataFrame([['']]).toDF("text")
re_model = pipeline.fit(empty_df)
text="""'MTH - Meritage Homes Corporation Reports Disappointing Revenue. RECN, Resources Connection Inc. Shareholder Raymond James Trust Has Decreased Holding'"""
light_model = nlp.LightPipeline(re_model)
light_model.fullAnnotate(text)
Results
| relation | entity1 | entity1_begin | entity1_end | chunk1 | entity2 | entity2_begin | entity2_end | chunk2 | confidence |
|-----------:|--------:|--------------:|------------:|-------:|--------:|--------------:|------------:|---------------------------:|-----------:|
| has_ticker | TICKER | 0 | 2 | MTH | ORG | 6 | 31 | Meritage Homes Corporation | 0.99532026 |
| has_ticker | TICKER | 64 | 67 | RECN | ORG | 70 | 93 | Resources Connection Inc | 0.97409964 |
Model Information
| Model Name: | finre_has_ticker |
| Compatibility: | Finance NLP 1.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 409.9 MB |
References
Manual annotations on tweets
Benchmarking
label Recall Precision F1 Support
has_ticker 0.717 0.827 0.768 60
Avg. 0.717 0.827 0.768 -
Weighted-Avg. 0.717 0.827 0.768 -