SparkNLP POS

Supported Labeling Types: Span Labeling

SparkNLP Part-of-Speech (POS) Tagging is a fast and scalable component of the SparkNLP library that assigns grammatical tags—such as noun, verb, adjective, or adverb—to each word in a sentence. It uses advanced NLP models optimized for large-scale processing, making it suitable for handling massive datasets efficiently. In our labeling platform, SparkNLP POS tagging enhances text analysis by providing syntactic insights that can improve label suggestions, rule-based automation, and overall annotation quality.

ML Assisted with SparkNLP POS

Model Details

• POS-tagging in SparkNLP is done via the en.pos model from johnsnowlabs/nlp_server.

• Models are trained primarily on the Penn Treebank corpus, supplemented with diverse web content to improve robustness across text types.

• Operates as a service accessible within the Datasaur Intelligence container.

Usage

• SparkNLP POS tagging is ideal for large-scale text processing, including syntactic analysis and document parsing.

• The tagset is similar to the NLTK provider.

References

• https://nlp.johnsnowlabs.com/docs/en/nlp_server/nlp_server

• https://nlu.johnsnowlabs.com/docs/en/examples#part-of-speech--pos