Learning Document-Level Semantic Properties from Free-Text Annotations

This paper demonstrates a new method for leveraging unstructured annotations to infer semantic document properties. We consider the domain of product reviews, which are often annotated by their authors with free-text keyphrases, such as "a real bargain" or "good value." We leverage these unstructured annotations by clustering them into semantic properties, and then tying the induced clusters to hidden topics in the document text. This allows us to predict relevant properties of unannotated documents. Our approach is implemented in a hierarchical Bayesian model with joint inference, which increases the robustness of the keyphrase clustering and encourages document topics to correlate with semantically meaningful properties. We perform several evaluations of our model, and find that it substantially outperforms alternative approaches.

• Type: Conference
• Year: 2008
• Where: ACL
• Authors: S. R. K. Branavan, Harr Chen, Jacob Eisenstein, Regina Barzilay
• Keywords: ACL 2008, Computational Linguistics, Semantic Document Properties, Semantic Properties, Unstructured Annotations