  Understanding a user's query intent behind a search is critical for modern
search engine success. Accurate query intent prediction allows the search
engine to better serve the user's need by rendering results from more relevant
categories. This paper aims to provide a comprehensive learning framework for
modeling query intent under different stages of a search. We focus on the
design for 1) predicting users' intents as they type in queries on-the-fly in
typeahead search using character-level models; and 2) accurate word-level
intent prediction models for complete queries. Various deep learning components
for query text understanding are experimented. Offline evaluation and online
A/B test experiments show that the proposed methods are effective in
understanding query intent and efficient to scale for online search systems.
