  Predicting user behaviour on a website is a difficult task, which requires
the integration of multiple sources of information, such as geo-location, user
profile or web surfing history. In this paper we tackle the problem of
predicting the user intent, based on the queries that were used to access a
certain webpage. We make no additional assumptions, such as domain detection,
device used or location, and only use the word information embedded in the
given query. In order to build competitive classifiers, we label a small
fraction of the EDI query intent prediction dataset
\cite{edi-challenge-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.
