onurgu/ner-tagger-dynet
See http://github.com/onurgu/joint-ner-and-md-tagger This repository is basically a Bi-LSTM based sequence tagger in both Tensorflow and Dynet which can utilize several sources of information about each word unit like word embeddings, character based embeddings and morphological tags from an FST to obtain the representation for that specific word unit.
This project helps natural language processing researchers train and evaluate neural network models for Named Entity Recognition (NER). It takes tokenized text in CoNLL format as input and outputs tagged files, identifying entities like people, organizations, and locations. Researchers working on improving the accuracy of NER systems, especially those incorporating morphological information, would find this useful.
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Use this if you are an NLP researcher working on named entity recognition and want to experiment with or reproduce models that leverage both word and morphological features.
Not ideal if you are a practitioner looking for a ready-to-use tool to tag text with named entities without needing to train or evaluate models.
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MIT
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Last pushed
Jul 06, 2018
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