CoreNLP POS

Supported Labeling Types: Span Labeling

CoreNLP Part-of-Speech (POS) Tagging is a feature of the Stanford CoreNLP toolkit that assigns grammatical categories—such as noun, verb, adjective, or adverb—to each word in a sentence. It uses probabilistic models trained on large annotated corpora to accurately analyze sentence structure. Within our labeling platform, CoreNLP POS tagging helps enhance text preprocessing, supports more accurate entity recognition, and enables advanced labeling workflows that rely on syntactic patterns or linguistic rules.

ML Assisted with CoreNLP POS

CoreNLP POS-tagging is done using CoreNLP Server using official pre-trained model invoked from fromnltk.parse.corenlp.CoreNLPParser.

Model Details

• CoreNLP POS-tagging is conducted using CoreNLP Server, leveraging the official pre-trained models.

• This system is invoked via from nltk.parse.corenlp.CoreNLPParser and uses a deep learning-based approach for accurate entity recognition.

• Operates as a service within the Datasaur Intelligence container, maintaining isolation while providing consistent access.

Usage

• This is ideal for complex linguistic analysis and tasks requiring detailed syntactic structures.

• The tagset is similar to the NLTK provider.

References

• https://nlp.stanford.edu/software/pos-tagger-faq.html

• https://stanfordnlp.github.io/CoreNLP/pos.html

• https://stanfordnlp.github.io/CoreNLP/corenlp-server.html

• https://www.nltk.org/api/nltk.parse.html#module-nltk.parse.corenlp

References

• UPenn Treebank Docs https://catalog.ldc.upenn.edu/docs/LDC99T42/

• python -c "import nltk; nltk.help.upenn_tagset()"