TheAnig/NER-LSTM-CNN-Pytorch
End-to-end-Sequence-Labeling-via-Bi-directional-LSTM-CNNs-CRF-Tutorial
This project helps natural language processing (NLP) engineers and data scientists build and understand systems that can identify and categorize key information within text. It takes raw text as input and outputs the same text with specific entities, like names of people, organizations, or locations, tagged and classified. This is primarily useful for those working on information extraction or text understanding tasks.
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Use this if you are a machine learning engineer or researcher focused on developing custom Named Entity Recognition (NER) models using PyTorch.
Not ideal if you need an out-of-the-box solution for NER without needing to understand or modify the underlying deep learning model.
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Jul 14, 2018
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