randomrandom/deep-atrous-ner
Deep-Atrous-CNN-NER: Word level model for Named Entity Recognition
This project helps data scientists and NLP practitioners automatically identify and categorize key entities like people, organizations, and locations within unstructured text. You input raw text documents, and it outputs the same text with specific words and phrases tagged by their entity type. This is useful for anyone who needs to extract structured information from large volumes of free-form text.
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Use this if you need to quickly and accurately find specific types of named entities in text data, especially if you have variable-length inputs and require faster prediction times.
Not ideal if your primary need is for a model that supports a wide variety of pre-configured datasets beyond the CoNLL-2003 format.
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Python
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Last pushed
Nov 24, 2017
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