1. Download the GloVe vectors with

make glove

2. Build the training data, train and evaluate the model with

make run

The training data must be in the IOB format

i O
need O
a O
flight O
from O
memphis B-fromloc.city_name
to O
las B-toloc.city_name
vegas I-toloc.city_name

python3 server.py

curl -X POST "http://localhost:5000/parse" -d "flight to new york from los angeles for next sunday"

Response

{
  "type": "",
  "departure": "LAX",
  "destination": "NYC",
  "departureDate": "2018-03-25",
  "departureTime": "",
  "returnDate": ""
}

• Improve char embedding Model
• Split Dataset
• Generate more data
• Add lexicon of cities, dates and times
• Move from BIO labels to BIOES (Begin, Inside, Outside, End, Single)
• Replace multi-digit numbers same as single-digit ones
• Split word before and after digit (ex: $5, 5pm)
• Try glove with 50d
• Explore FastText model
• Reduce out-of-training words

https://arxiv.org/pdf/1511.08308.pdf

https://arxiv.org/pdf/1603.01354.pdf

https://guillaumegenthial.github.io/sequence-tagging-with-tensorflow.html

https://github.com/guillaumegenthial/sequence_tagging