Query Intent Detection with Deep Learning
Posted: 28 Jan 2021
Date Written: November 4, 2020
Predicting intent of the user query is one of the fundamental tasks in building information retrieval systems. To solve this task, we present a deep learning method to classify user search queries into one of "get a doc", "topic search", "entity", and "locate source" classes. In this work, models are built and evaluated with actual user queries in Lexis Advance. At a high-level, our method consists of three components i.e. data augmentation, glove embeddings, and convolutional networks algorithm. This approach produced very good accuracy about 87% if we know the query belongs to one of the above four intents and very efficient at runtime by taking only 2 milliseconds per query and standard ec2 instance. Even though we evaluated our approach on four intents, this method applicable for general purpose classification of user queries and to use in production systems.
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