monikkinom/ner-lstm
Named Entity Recognition using multilayered bidirectional LSTM
This project helps identify and categorize important entities like people, organizations, and locations within large amounts of text. You input raw text documents (like news articles or reports) and get back the same text with named entities highlighted and labeled. This is ideal for natural language processing specialists or data scientists working with textual data.
537 stars. No commits in the last 6 months.
Use this if you need to automatically extract and classify specific types of information from unstructured text efficiently.
Not ideal if you're looking for a simple, out-of-the-box solution without any programming or model training steps.
Stars
537
Forks
180
Language
Python
License
—
Category
Last pushed
Mar 10, 2019
Commits (30d)
0
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