sparknlp.annotator.token.chunk_tokenizer#

Classes#

class ChunkTokenizer[source]#

Tokenizes and flattens extracted NER chunks.

The ChunkTokenizer will split the extracted NER CHUNK type Annotations and will create TOKEN type Annotations. The result is then flattened, resulting in a single array.

Input Annotation types	Output Annotation type
CHUNK	TOKEN

Parameters:

None

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> sparknlp.common import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> sentenceDetector = SentenceDetector() \
...     .setInputCols(["document"]) \
...     .setOutputCol("sentence")
>>> tokenizer = Tokenizer() \
...     .setInputCols(["sentence"]) \
...     .setOutputCol("token")
>>> entityExtractor = TextMatcher() \
...     .setInputCols(["sentence", "token"]) \
...     .setEntities("src/test/resources/entity-extractor/test-chunks.txt", ReadAs.TEXT) \
...     .setOutputCol("entity")
>>> chunkTokenizer = ChunkTokenizer() \
...     .setInputCols(["entity"]) \
...     .setOutputCol("chunk_token")
>>> pipeline = Pipeline().setStages([
...         documentAssembler,
...         sentenceDetector,
...         tokenizer,
...         entityExtractor,
...         chunkTokenizer
... ])
>>> data = spark.createDataFrame([
...     ["Hello world, my name is Michael, I am an artist and I work at Benezar"],
...     ["Robert, an engineer from Farendell, graduated last year. The other one, Lucas, graduated last week."]
>>> ]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("entity.result as entity" , "chunk_token.result as chunk_token").show(truncate=False)
+-----------------------------------------------+---------------------------------------------------+
|entity                                         |chunk_token                                        |
+-----------------------------------------------+---------------------------------------------------+
|[world, Michael, work at Benezar]              |[world, Michael, work, at, Benezar]                |
|[engineer from Farendell, last year, last week]|[engineer, from, Farendell, last, year, last, week]|
+-----------------------------------------------+---------------------------------------------------+

name = 'ChunkTokenizer'[source]#

inputAnnotatorTypes[source]#

class ChunkTokenizerModel(classname='com.johnsnowlabs.nlp.annotators.ChunkTokenizerModel', java_model=None)[source]#

Instantiated model of the ChunkTokenizer.

This is the instantiated model of the ChunkTokenizer. For training your own model, please see the documentation of that class.

Input Annotation types	Output Annotation type
CHUNK	TOKEN

Parameters:

None

name = 'ChunkTokenizerModel'[source]#

inputAnnotatorTypes[source]#