ANNOTATE: orgANizing uNstructured cOntenTs viA Topic labEls

With the advent of Big Data paradigm, filtering, retrieval, and linking of unstructured multi-modal data has become a necessity. Assigning topic labels to contents, that accurately capture the meaning and contextual information, is a fundamental problem in organizing unstructured data. The usage of manually-assigned tags for this purpose introduces inconsistencies because of different »surface forms». On the other hand, existing automated approaches either use hierarchical multi-label classification, or are unsupervised and rely on (undirected) graph measures leveraging taxonomies. While the former requires large training data set to learn the characteristics of each topic class, the latter lacks the flexibility to learn broad range of related topics and are less accurate. We propose a novel framework, ANNOTATE based on a small set of features and directed traversal of taxonomies to learn a broad spectrum of related topics using limited training data. We also show that our approach provides accurate labels for several domains without the need for re-training. For instance, the framework, trained on a small set of BBC news articles, exhibits close matches to user-generated tags for Quora documents. Experimental results, on the same model, for news classification and identifying aspects of Amazon product reviews, based on Amazon Mechanical Turk evaluation show our approach to be significantly better than state-of-the-art. We further present real-life case studies of our proposed framework for automatically tagging Quora posts, and topically segmenting, indexing and linking related YouTube videos (using our publicly available Chrome browser extension).