  User intent classification is an important task in information retrieval. In
this work, we introduce a revised taxonomy of user intent. We take the widely
used differentiation between navigational, transactional and informational
queries as a starting point, and identify three different sub-classes for the
informational queries: instrumental, factual and abstain. The resulting
classification of user queries is more fine-grained, reaches a high level of
consistency between annotators, and can serve as the basis for an effective
automatic classification process. The newly introduced categories help
distinguish between types of queries that a retrieval system could act upon,
for example by prioritizing different types of results in the ranking.We have
used a weak supervision approach based on Snorkel to annotate the ORCAS dataset
according to our new user intent taxonomy, utilising established heuristics and
keywords to construct rules for the prediction of the intent category. We then
present a series of experiments with a variety of machine learning models,
using the labels from the weak supervision stage as training data, but find
that the results produced by Snorkel are not outperformed by these competing
approaches and can be considered state-of-the-art. The advantage of a
rule-based approach like Snorkel's is its efficient deployment in an actual
system, where intent classification would be executed for every query issued.
The resource released with this paper is the ORCAS-I dataset: a labelled
version of the ORCAS click-based dataset of Web queries, which provides 18
million connections to 10 million distinct queries.
