Learning Document-Level Semantic Properties from Free-Text Annotations

This paper demonstrates a new method for leveraging free-text annotations to infer semantic properties of documents. Free-text annotations are becoming increasingly abundant, due to the recent dramatic growth in semistructured, user-generated online content. An example of such content is product reviews, which are often annotated by their authors with pros/cons keyphrases such as “a real bargain” or “good value.” To exploit such noisy annotations, we simultaneously find a hidden paraphrase structure of the keyphrases, a model of the document texts, and the underlying semantic properties that link the two. This allows us to predict properties of unannotated documents. Our approach is implemented as a hierarchical Bayesian model with joint inference, which increases the robustness of the keyphrase clustering and encourages the document model to correlate with semantically meaningful properties. We perform several evaluations of our model, and find that it substantially outperforms alternative approaches.