Robust Natural Language Processing for Urban Trip Planning
Joel Booth, Barbara Di Eugenio, Isabel F. Cruz, Ouri Wolfson · Applied Artificial Intelligence · 2015
Navigating large, urban transportation networks is a complicated task. A user needs to negotiate the available modes of transportation, their schedules, and how they are interconnected. In this article we present a Natural Language interface for trip planning in complex, multimodal, urban transportation networks. Our objective is to provide robust understanding of complex requests while giving the user flexibility in their language.We designed TRANQUYL, a transportation query language for trip planning; we developed a user-centric ontology, which defines the concepts covered by the interface and allows for a broad vocabulary. NL2TRANQUYL, the software system built on these foundations, translates English requests into formal TRANQUYL queries. Through a detailed intrinsic evaluation, we show that NL2TRANQUYL is highly accurate and robust with respect to paraphrasing requests, as well as handling fragmented or telegraphic requests.