Coreference resolution models produce a mapping of all the expressions in a text that refer to the same real-world entity. Coreference resolution can be a challenging task, particularly in cases where there are multiple potential referents for a given expression, or when the referent is implicit or ambiguous.
See how Bert-based models in Spark NLP can effortlessly resolve co-reference in your text data Coreference resolution is the task of identifying and linking all expressions within a text that...
Using Spark NLP in Python to identify named entities in texts at scale. Named Entity Recognition with Python TL;DR: Named Entity Recognition (NER) is a Natural Language Processing (NLP) technique...
Using machine learning and regex patterns to identify and extract date information in Spark NLP TL; DR: Dates extraction from a text is a common Natural Language Processing (NLP) task...
Creating visualizations for analysis and reporting using Spark NLP and Spark NLP display. TL;DR: Visualizing Named Entities is a technique for representing the results of named entity recognition (NER) in...
Use pretrained models, segment texts into words, and train custom word segmenter models with Python. TL; DR: Some Asian languages don’t separate words by white space like English, and NLP...
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