  Slot-filling refers to the task of annotating individual terms in a query
with the corresponding intended product characteristics (product type, brand,
gender, size, color, etc.). These characteristics can then be used by a search
engine to return results that better match the query's product intent.
Traditional methods for slot-filling require the availability of training data
with ground truth slot-annotation information. However, generating such labeled
data, especially in e-commerce is expensive and time-consuming because the
number of slots increases as new products are added. In this paper, we present
distant-supervised probabilistic generative models, that require no manual
annotation. The proposed approaches leverage the readily available historical
query logs and the purchases that these queries led to, and also exploit
co-occurrence information among the slots in order to identify intended product
characteristics. We evaluate our approaches by considering how they affect
retrieval performance, as well as how well they classify the slots. In terms of
retrieval, our approaches achieve better ranking performance (up to 156%) over
Okapi BM25. Moreover, our approach that leverages co-occurrence information
leads to better performance than the one that does not on both the retrieval
and slot classification tasks.
