Predicting User Search Intent Using CNNS and RNNS

In this paper we tackle the problem of predicting the user intent, based on the queries that were used to access a certain webpage.

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.