sudhamstarun/AwesomeNER
An implementation of bidirectional LSTM-CRF for Named Entity Relationship on custom corpus with custom word embeddings
This project helps financial analysts and researchers automatically identify specific entities like company names, people, or locations within financial texts. You feed it raw financial documents or sentences, and it highlights and categorizes these key pieces of information. It's designed for anyone working with large volumes of financial data who needs to quickly extract structured insights.
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Use this if you need to extract specific, named entities from financial documents or text that traditional methods struggle with.
Not ideal if your primary need is for general text analysis outside of the finance domain or if you require an off-the-shelf solution for non-finance-specific tasks.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Apr 09, 2019
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