Labeling Documents in Search Collection: Evolving Classifiers on a Semantically Relevant Label Space.
Ramakrishna Bairi, Ganesh Ramakrishnan · 2014
Associating meaningful label or category information with every document in a search collection could help in improv-ing retrieval effectiveness. However, identifying the right choice of category tags for organizing and representing a large digital library of documents is a challenging task. A completely automated approach to category creation from the underlying collection could be prone to noise. On the other hand, an absolutely manual approach to the creation of categories could be cumbersome and expensive. Through this work, we propose an intermediate solution, in which, a global, collaboratively-developed Knowledge Graph of cat-egories can be adapted to a local document categorization problem over the search collection effectively. We model our classification problem as that of inferring structured labels in an Associative Markov Network meta-model over SVMs,