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Understanding a users query intent behind a search is critical for modern search engine success. Accurate query intent prediction allows the search engine to better serve the users 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.
As a popular Q&A site for programming, Stack Overflow is a treasure for developers. However, the amount of questions and answers on Stack Overflow make it difficult for developers to efficiently locate the information they are looking for. There are
LiveSketch is a novel algorithm for searching large image collections using hand-sketched queries. LiveSketch tackles the inherent ambiguity of sketch search by creating visual suggestions that augment the query as it is drawn, making query specifica
We study the problem of query attribute value extraction, which aims to identify named entities from user queries as diverse surface form attribute values and afterward transform them into formally canonical forms. Such a problem consists of two phas
Conversational search plays a vital role in conversational information seeking. As queries in information seeking dialogues are ambiguous for traditional ad-hoc information retrieval (IR) systems due to the coreference and omission resolution problem
Understanding search queries is critical for shopping search engines to deliver a satisfying customer experience. Popular shopping search engines receive billions of unique queries yearly, each of which can depict any of hundreds of user preferences