https://github.com/kamalkraj/named-entity-recognition-with-bidirectional-lstm-cnns

Named-Entity-Recognition-with-Bidirectional-LSTM-CNNs
https://github.com/kamalkraj/named-entity-recognition-with-bidirectional-lstm-cnns

bilstm character-embeddings cnn conll-2003 glove-embeddings keras named-entity-recognition python36 tensorflow word-embeddings

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Named-Entity-Recognition-with-Bidirectional-LSTM-CNNs

• Host: GitHub
• URL: https://github.com/kamalkraj/named-entity-recognition-with-bidirectional-lstm-cnns
• Owner: kamalkraj
• License: gpl-3.0
• Created: 2018-01-27T13:27:39.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2020-04-21T09:34:09.000Z (almost 5 years ago)
• Last Synced: 2025-01-14T00:09:20.595Z (7 days ago)
• Topics: bilstm, character-embeddings, cnn, conll-2003, glove-embeddings, keras, named-entity-recognition, python36, tensorflow, word-embeddings
• Language: Python
• Homepage:
• Size: 1.09 MB
• Stars: 361
• Watchers: 22
• Forks: 142
• Open Issues: 13
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
#### 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

   

 

 # Network Model Constructed Using Keras

 ![alt text](https://raw.githubusercontent.com/kamalkraj/Named-Entity-Recognition-with-Bidirectional-LSTM-CNNs/master/model.png)

 

 ## 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')]

```