Learning to Tag Text from Rules and Examples

Abstract

Tagging has become a popular way to improve the access to resources, especially in social networks and folksonomies. Most of the resource sharing tools allow a manual labeling of the available items by the community members. However, the manual approach can fail to provide a consistent tagging especially when the dimension of the vocabulary of the tags increases and, consequently, the users do not comply to a shared semantic knowledge. Hence, automatic tagging can provide an effective way to complete the manual added tags, especially for dynamic or very large collections of documents like the Web. However, when an automatic text tagger is trained over the tags inserted by the users, it may inherit the inconsistencies of the training data. In this paper, we propose a novel approach where a set of text categorizers, each associated to a tag in the vocabulary, are trained both from examples and a higher level abstract representation consisting of FOL clauses that describe semantic rules constraining the use of the corresponding tags. The FOL clauses are compiled into a set of equivalent continuous constraints, and the integration between logic and learning is implemented in a multi-task learning scheme. In particular, we exploit the kernel machine mathematical apparatus casting the problem as primal optimization of a function composed of the loss on the supervised examples, the regularization term, and a penalty term deriving from forcing the constraints resulting from the conversion of the logic knowledge. The experimental results show that the proposed approach provides a significant accuracy improvement on the tagging of bibtex entries.