Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/ContextScout/gcn_ner
Graph Convolutional neural network named entity recognition
https://github.com/ContextScout/gcn_ner
Last synced: 3 months ago
JSON representation
Graph Convolutional neural network named entity recognition
- Host: GitHub
- URL: https://github.com/ContextScout/gcn_ner
- Owner: ContextScout
- License: other
- Created: 2017-09-08T22:52:24.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2017-10-12T11:16:52.000Z (over 6 years ago)
- Last Synced: 2024-01-16T19:14:27.031Z (5 months ago)
- Language: Python
- Size: 113 MB
- Stars: 137
- Watchers: 9
- Forks: 31
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Lists
- awesome-gcn - ContextScout/gcn_ner
README
NER that uses Graph Conv Nets
=============================
This is an implementation of a named entity recognizer that uses Graph
Convolutional Networks. The reference article is [Graph Convolutional
Networks for Named Entity
Recognition](https://arxiv.org/abs/1709.10053).
This code uses GCNs and POS tagging to boost the entity recognition of a bidirectional LSTM.
It scores ~81% on the Ontonotes 5 test dataset, which can be retrieved from
[the LDC website](https://catalog.ldc.upenn.edu/ldc2013t19).
The system currently uses the word vectors that come with spacy's "en_core_web_md" model.
Installation
------------
```bash
git clone https://github.com/contextscout/gcn_ner.git
cd gcn_ner
virtualenv --python=/usr/bin/python3 .env
source .env/bin/activate
pip install -r requirements.txt
python -m spacy download en
python -m spacy download en_core_web_md
```
if you want to install Tensorflow with GPU capabilities please use
```python
pip install -r requirements_gpu.txt
```
Test NER on a text
--------
Execute the file
```python
python test_ner.py < data/random_text.txt
```
Train NER from a dataset
---------
You will need to put your 'train.conll' into the 'data/' directory,
then execute the file
```python
python train.py
```
Test the dataset F1 score
-------------------------
You will need to put your 'dev.conll' or 'test.conll' into the 'data/' directory,
then execute the file
```python
python test_dataset.py
```
CONLL format
------------
The training/testing conll files must be in the conll format, as in the following example.
Only the fourth, fifth, and eleventh columns are used.
```code
source_file_name 1 0 New NNP (TOP(S(NP* - - - Speaker#1 (GPE* * (ARG1* (ARG1* (19
source_file_name 1 1 York NNP *) - - - Speaker#1 *) * *) *) 19)
source_file_name 1 2 was VBD (VP* be 03 - Speaker#1 * (V*) * * -
source_file_name 1 3 developed VBN (VP* develop 02 - Speaker#1 * * (V*) * -
source_file_name 1 4 from IN (PP* - - - Speaker#1 * * (ARG2* * -
source_file_name 1 5 a DT (NP* - - - Speaker#1 * * * * -
source_file_name 1 6 hunting NN * - - - Speaker#1 * * * * -
source_file_name 1 7 harbor NN *)) - - - Speaker#1 * * *) * -
source_file_name 1 8 one CD (ADVP(NP(QP* - - - Speaker#1 (DATE* * (ARGM-TMP* * -
source_file_name 1 9 million CD *) - - - Speaker#1 * * * * -
source_file_name 1 10 years NNS *) - - - Speaker#1 * * * * -
source_file_name 1 11 ago RB *) - - - Speaker#1 *) * *) * -
source_file_name 1 12 to TO (S(VP* - - - Speaker#1 * * (ARGM-PRP* * -
source_file_name 1 13 become VB (VP* become 01 1 Speaker#1 * * * (V*) -
source_file_name 1 14 today NN (NP(NP* - - - Speaker#1 (DATE) * * (ARG2* -
source_file_name 1 15 's POS *) - - - Speaker#1 * * * * -
source_file_name 1 16 international JJ * - - - Speaker#1 * * * * -
source_file_name 1 17 metropolis NNS *)))))) - - - Speaker#1 * * *) *) -
```