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Classifying search queries in: informational, navigation and transactional.
In this paper we tackle the problem of predicting the user intent, based on the queries that were used to access a certain
In order to build competitive classifiers, we label a small fraction of the query intent prediction dataset, which is used as ground truth.
Then, using various rule-based approaches, we automatically label the rest of the dataset, train the
classifiers and evaluate the quality of the automatic labeling on the ground truth dataset.
We used both recurrent and convolutional networks as the models, while representing the words in the query with multiple embedding methods.