Healthcare NLP employs advanced filtering techniques to refine entity recognition by excluding irrelevant entities based on specific criteria like whitelists or regular expressions. This approach is essential for ensuring precision...

In this notebook, RoBertaForQuestionAnswering was used for versatile Named Entity Recognition (NER) without extensive domain-specific training. This blog post walks through the ZeroShotNerModel implementation and explores its ability to adapt...

Leveraging TextMatcherInternal for Precise Phrase Matching in Healthcare Texts The TextMatcherInternal annotator in Healthcare NLP is a powerful tool for exact phrase matching in healthcare text analysis. We’ll cover its...

ChunkConverter unifies regex and NER entity extractions in Spark NLP pipelines by converting regex chunks to a standard chunk format with entity labels, enabling integrated downstream processing. What is ChunkConverter?...

EntityRulerInternal in Spark NLP extracts medical entities from text using regex patterns or exact matches defined in JSON or CSV files. With practical examples, this post explains how to set...