Introduction

This project is a journaled hands-on implementation of an assistance software that can answer to a subset of questions found in ATIS, a well-known airplane ticket reservation system dataset. The goal is to answer to flight-related inquiry written in plain english.

Outline

The project includes the following:

• A collection of jupyter notebooks to demonstrate the techniques used for building the NLP pipelines
• A jupyter notebook which showcases how to cleanse data to adjust it to the application's needs
• An end-to-end NLU application to demonstrate a practical end result

NLP Techniques

The notebooks are split into the following 5 chapters:

0. Introduce and explore ATIS dataset

1. Extract the named entities with two different methods:

   • with spaCy Matcher
   • by walking on the dependency tree

2. Determine the intent of the user utterance using various techniques:

   • by extracting the verbs and their direct objects
   • by using wordlists
   • by walking on the dependency tree

3. Match keywords to synonyms:

   • from a synonyms list to detect semantic similarity
   • with word vector-based semantic similarity methods

4. Generate a semantic representation

Data Engineering

The following datasets are used to compile a sqlite database:

• Kaggle: flight data.csv
• OpenFlights: airports-extended.dat

End-to-end System

A flight information assistance application which searches a database with queries written in plain english.