  Contextual advertising provides advertisers with the opportunity to target
the context which is most relevant to their ads. However, its power cannot be
fully utilized unless we can target the page content using fine-grained
categories, e.g., "coupe" vs. "hatchback" instead of "automotive" vs. "sport".
The widely used advertising content taxonomy (IAB taxonomy) consists of 23
coarse-grained categories and 355 fine-grained categories. With the large
number of categories, it becomes very challenging either to collect training
documents to build a supervised classification model, or to compose
expert-written rules in a rule-based classification system. Besides, in
fine-grained classification, different categories often overlap or co-occur,
making it harder to classify accurately. In this work, we propose wiki2cat, a
method to tackle the problem of large-scaled fine-grained text classification
by tapping on Wikipedia category graph. The categories in IAB taxonomy are
first mapped to category nodes in the graph. Then the label is propagated
across the graph to obtain a list of labeled Wikipedia documents to induce text
classifiers. The method is ideal for large-scale classification problems since
it does not require any manually-labeled document or hand-curated rules or
keywords. The proposed method is benchmarked with various learning-based and
keyword-based baselines and yields competitive performance on both publicly
available datasets and a new dataset containing more than 300 fine-grained
categories.
