Bootstrapping Large-Scale Fine-Grained Contextual Advertising Classifier from W ikipedia (2021)

Abstract: Contextual advertising provides advertisers with the opportunity to target the context which is most relevant to their ads. The large variety of potential topics makes it very challenging to collect training documents to build a supervised classification model or 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 large-scaled fine-grained text classification by tapping on the Wikipedia category graph. The categories in the 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 publicly available datasets and a new dataset containing more than 300 fine-grained categories.

## introduction despite the fast advancement of text classification technologies, most text classification models are trained and applied to a relatively small number of categories. popular benchmark datasets contain from two up to tens of categories, such as sst2 dataset for sentiment classification (2 categories) @xcite , ag news dataset (4 categories) @xcite and 20 newsgroups dataset @xcite for topic classification. in the meantime, industrial applications often involve fine-grained classification with a large number of categories. for example, walmart built a hybrid classifier to categorize products into 5000+ product categories @xcite , and yahoo built a contextual advertising classifier with a taxonomy of around 6000 categories @xcite . unfortunately, both systems require a huge human effort in composing and maintaining rules and keywords. readers can neither reproduce their system nor is the system or data available for comparison. in this work, we focus on the application of contextual advertising @xcite , which allows advertisers to target the context most relevant to their ads. however, we cannot fully utilize its power unless we can target the page content using fine-grained categories, e.g., "coupé"' vs. "hatchback" instead of "automotive" vs. "sport". this motivates a classification taxonomy with both high coverage and high granularity. the commonly used contextual taxonomy introduced by interactive advertising bureau (iab) contains 23 coarse-grained categories and 355 fine-grained categories foot_0 . figure 1 shows a snippet of the taxonomy. large online encyclopedias, such as wikipedia, contain an updated account of almost all topics. therefore, we ask an essential question: can we bootstrap a text classifier with hundreds of categories from wikipedia without any manual labeling? we tap on and extend previous work on wikipedia content analysis @xcite to automatically label wikipedia articles related to each category in our taxonomy by wikipedia category graph traversal. we then train classification models with the labeled wikipedia articles. we compare our method with various learning-based and keyword-based baselines and obtain a competitive performance. ## method we propose wiki2cat, a simple framework using wikipedia to bootstrap text categorizers. we first map the target taxonomy to corresponding wikipedia categories (briefed in section 3.1). we then traverse the wikipedia category graph to automatically label wikipedia articles (section 3.2). finally, we induce a classifier from the labeled wikipedia articles (section 3.3). figure 2 overviews the end-to-end process of building classifiers under the wiki2cat framework. ## conclusions and future work we introduced wiki2cat, a simple framework to bootstrap large-scale fine-grained text classifiers from wikipedia without having to label any document manually. the method was benchmarked on both coarse-grained and fine-grained contextual advertising datasets and achieved competitive performance against various baselines. it performed especially well on fine-grained classification, which both is more challenging and requires more manual labeling in a fully-supervised setting. as an ongoing effort, we are exploring using unlabeled in-domain documents for domain adaptation to achieve better accuracy.