This package wraps Stanford CoreNLP annotators as Spark DataFrame functions following the simple APIs introduced in Stanford CoreNLP 3.7.0.

This package requires Java 8 and CoreNLP to run. Users must include CoreNLP model jars as dependencies to use language models.

All functions are defined under com.databricks.spark.corenlp.functions.

• cleanxml: Cleans XML tags in a document and returns the cleaned document.
• tokenize: Tokenizes a sentence into words.
• ssplit: Splits a document into sentences.
• pos: Generates the part of speech tags of the sentence.
• lemma: Generates the word lemmas of the sentence.
• ner: Generates the named entity tags of the sentence.
• depparse: Generates the semantic dependencies of the sentence and returns a flattened list of (source, sourceIndex, relation, target, targetIndex, weight) relation tuples.
• coref: Generates the coref chains in the document and returns a list of (rep, mentions) chain tuples, where mentions are in the format of (sentNum, startIndex, mention).
• natlog: Generates the Natural Logic notion of polarity for each token in a sentence, returned as up, down, or flat.
• openie: Generates a list of Open IE triples as flat (subject, relation, target, confidence) tuples.
• sentiment: Measures the sentiment of an input sentence on a scale of 0 (strong negative) to 4 (strong positive).

Users can chain the functions to create pipeline, for example:

import org.apache.spark.sql.functions._ import com.databricks.spark.corenlp.functions._ val input = Seq( (1, "<xml>Stanford University is located in California. It is a great university.</xml>") ).toDF("id", "text") val output = input .select(cleanxml('text).as('doc)) .select(explode(ssplit('doc)).as('sen)) .select('sen, tokenize('sen).as('words), ner('sen).as('nerTags), sentiment('sen).as('sentiment)) output.show(truncate = false)

+----------------------------------------------+------------------------------------------------------+--------------------------------------------------+---------+ |sen |words |nerTags |sentiment| +----------------------------------------------+------------------------------------------------------+--------------------------------------------------+---------+ |Stanford University is located in California .|[Stanford, University, is, located, in, California, .]|[ORGANIZATION, ORGANIZATION, O, O, O, LOCATION, O]|1 | |It is a great university . |[It, is, a, great, university, .] |[O, O, O, O, O, O] |4 | +----------------------------------------------+------------------------------------------------------+--------------------------------------------------+---------+ 

If you are a Databricks user, please follow the instructions in this example notebook.

Because CoreNLP depends on protobuf-java 3.x but Spark 2.4 depends on protobuf-java 2.x, we release spark-corenlp as an assembly jar that includes CoreNLP as well as its transitive dependencies, except protobuf-java being shaded. This might cause issues if you have CoreNLP or its dependencies on the classpath.

To use spark-corenlp, you need one of the CoreNLP language models:

# Download one of the language models.  wget http://repo1.maven.org/maven2/edu/stanford/nlp/stanford-corenlp/3.9.1/stanford-corenlp-3.9.1-models.jar # Run spark-shell  spark-shell --packages databricks/spark-corenlp:0.4.0-spark_2.4-scala_2.11 --jars stanford-corenlp-3.9.1-models.jar

Many thanks to Jason Bolton from the Stanford NLP Group for API discussions.