双向LSTM-CNN的命名实体识别：双向LSTM-CNN的命名实体识别

#### Better NER [BERT Named-Entity-Recognition](https://github.com/kamalkraj/BERT-NER) # Named-Entity-Recognition-with-Bidirectional-LSTM-CNNs A keras implementation of Bidirectional-LSTM_CNNs for Named-Entity-Recoganition. The original paper can be found at https://arxiv.org/abs/1511.08308 The implementation differs from the original paper in the following ways : 1) lexicons are not considered 2) Bucketing is used to speed up the training 3) nadam optimizer used instead of SGD # Result The model produces a test F1_score of 90.9 % with ~70 epochs. The results produced in the paper for the given architecture is 91.14 Architecture(BILSTM-CNN with emb + caps) # Dataset ### conll-2003 # Network Model in paper <img src="https://raw.githubusercontent.com/kamalkraj/Named-Entity-Recognition-with-Bidirectional-LSTM-CNNs/master/model_on_paper.png"/> <img src="https://raw.githubusercontent.com/kamalkraj/Named-Entity-Recognition-with-Bidirectional-LSTM-CNNs/master/char_embeddings.png"/> # Network Model Constructed Using Keras  ## To run the script ```bash python3 nn.py ``` ## Requirements 0) nltk 1) numpy 2) Keras==2.1.2 3) Tensorflow==1.4.1 ## Inference on trained model ```python from ner import Parser p = Parser() p.load_models("models/") p.predict("Steve Went to Paris") ##Output [('Steve', 'B-PER'), ('went', 'O'), ('to', 'O'), ('Paris', 'B-LOC')] ```