Sequence Tagging with Tensorflow

This repo implements a sequence tagging model using tensorflow. Check my blog post

Task

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

Model

Similar to this paper.

• 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

Data

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

Usage

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