No abstract

Understanding interests expressed through user's search query is a task of critical importance for many internet applications. To help identify user interests, web engines commonly utilize classification of queries into one or more predefined interest categories. However, majority of the queries are noisy short texts, making accurate classification a challenging task. In this demonstration, we present queryCategorizr, a novel semi-supervised learning system that embeds queries into low-dimensional vector space using a neural language model applied on search log sessions, and classifies them into general interest categories while relying on a small set of labeled queries. Empirical results on large-scale data show that queryCategorizr outperforms the current stateof-the-art approaches. In addition, we describe a Graphical User Interface (GUI) that allows users to query the system and explore classification results in an interactive manner.