David Batista

Comtravo's data-science team assists travel agents by automating certain steps in the offer and booking process given a certain travel request. One of these automation steps consists in automatically extracting all the necessary and relevant elements of a booking request sent via an email. To handle email bookings we developed a complex NLP pipeline, which analyses incoming booking requests by identifying and classifying all the necessary elements in the request in order to automatically generate offers that can fulfill a booking request. The NLP pipeline is composed of several different modules. First, a custom tokenization processed is applied and the tokens are stored in a memory-efficient representation of the message; then, we perform a simple information extraction task by recognizing a custom set of named-entities from the message, next we apply several message level classifiers identifying wether the message is in fact a booking request and what type of travel items are present (e.g.: Hotel, Flight, Train, etc.); knowing the scope of the message and having named-entities we apply a slot labeler module which assigns each named-entity to a particular slot (e.g.: destination, origin, departure time, etc.). Finally, we have a semantification process, e.g.: airports are assigned IATA codes, time expressions are grounded to a date or time range, train stations are mapped to IBNR codes. In my presentation I will describe the developed pipeline, giving an overview of our NLP stack and point out some of the challenges.