This repo implements a sequence tagging model using tensorflow.
State-of-the-art performance (F1 score close to 91).
Check my blog post
Given a sentence, give a tag to each word. A classical application is Named Entity Recognition (NER). Here is an example
John lives in New York
PER O O LOC LOC
Similar to Lample et al. and Ma and Hovy.
- concatenate final states of a bi-lstm on character embeddings to get a character-based representation of each word
- concatenate this representation to a standard word vector representation (GloVe here)
- run a bi-lstm on each sentence to extract contextual representation of each word
- decode with a linear chain CRF
The training data must be in the following format (identical to the CoNLL2003 dataset).
John B-PER
lives O
in O
New B-LOC
York I-LOC
. O
This O
is O
another O
sentence
Once you have produced your txt files, change the parameters in config.py like
# dataset
dev_filename = "data/coNLL/eng/eng.testa.iob"
test_filename = "data/coNLL/eng/eng.testb.iob"
train_filename = "data/coNLL/eng/eng.train.iob"
You also need to download GloVe vectors.
First, build vocab from the data and extract trimmed glove vectors according to the config in config.py.
python build_data.py
Second, train and test model with
python main.py
Data iterators and utils are in data_utils.py and the model with training/test procedures are in model.py
Training time on NVidia Tesla K80 is 110 seconds per epoch on CoNLL train set using characters embeddings and CRF.
This project is licensed under the terms of the apache 2.0 license (as Tensorflow and derivatives). If used for research, citation would be appreciated.