https://github.com/opringle/named_entity_recognition

A place to implement state of the art deep learning methods for named entity recognition using python and MXNet.
https://github.com/opringle/named_entity_recognition

Last synced: 2 months ago
JSON representation

A place to implement state of the art deep learning methods for named entity recognition using python and MXNet.

• Host: GitHub
• URL: https://github.com/opringle/named_entity_recognition
• Owner: opringle
• Created: 2017-12-09T21:36:35.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2018-04-14T21:49:04.000Z (over 6 years ago)
• Last Synced: 2024-08-01T22:41:47.093Z (5 months ago)
• Language: Python
• Homepage:
• Size: 4.83 MB
• Stars: 5
• Watchers: 2
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• Awesome-MXNet - NER with Bidirectional LSTM-CNNs

README

        
## Goal

- This repo contains an MXNet implementation of this state of the art [entity recognition model](https://www.aclweb.org/anthology/Q16-1026).

- You can find my blog post on the model [here](https://opringle.github.io/2018/02/06/CNNLSTM_entity_recognition.html).

## Running the code

1. Clone this repo

2. Train the model: `$ cd src && python ner.py`

To reproduce the preprocessed training data:

1. Download and unzip the data: https://www.kaggle.com/abhinavwalia95/entity-annotated-corpus/downloads/ner_dataset.csv

2. Move ner_dataset.csv into `./data`

3. `$ cd src && python preprocess.py`