NER Travel Queries

This includes two different implementations to solve the Airline Travel Information System(ATIS) Named-Entity-Recognition (NER) challenge.

One implementation uses (linear chain) conditional random fields (CRF), and, more specifically, the python-crfsuite library as its basis.

The other implementation uses a single-layer LSTM leveraging Keras and Tensorflow.

The LSTM approach resulted in better performance in terms of precision and recall.

Here is an example sentence and its labels from the dataset:

Show (O) | flights (O) | from (O) | Boston (B-dept) | to (O) | New (B-arr) | York (I-arr) | today (B-depart_date.today_relative)

ATIS Data

Download ATIS Dataset here! split 0 split 1 split 2 split 3 split 4

Installation

• Python 3.5
• virtualenv venv && source venv/bin/activate
• pip install -r requirements.txt
• Execute python download_data.py
• Execute python -m spacy download en
• Specify tensorflow as a backend of Keras in file ~/.keras/keras.json

Code

CRF

• The python file crf.training.py performs Random Grid search (optimizing F1 score) to find the best possible values for parameters c1 and c2 of CRF. It saves the model in a file named best_crf_model.pkl. If you run it again, it will overwrite the best_crf_model.pkl file. It takes ~7 hours in ` MacBook Pro i7 16GB.
• The python file crf.evaluation.py evaluates the latter model in terms of Precision, Recall and Sequence Accuracy Score.

LSTM (Long short-term memory neural network)

• The python file lstm.training.py trains/evaluates a single-layer LSTM network. The weights for each epoch are stored under lstm/keras_checkpoints/. It takes a few hours in CPU at most.

Performance

CRF

• Weighted Precision Score = 0.958208628893
• Weighted Recall Score = 0.96260056534
• Sequence Accuracy Score = 0.7928331466965286
• crf_results.txt has thorough details about the performance of CRF in this task

LSTM

• Weighted Precision Score = 0.972639507033
• Weighted Recall Score = 0.97347249402
• Sequence Accuracy Score = 0.8286674132138858
• lstm_results.txt has thorough details about the performance of LSTM in this task

Future Work

Try more complicated LSTM architectures and/or use pre-trained word embeddings.