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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.” | Learning Document-Level Semantic Properties from Free-text Annotations S.R.K. Branavan Harr Chen Jacob Eisenstein Regina Barzilay Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology branavan harr jacobe regina @csail.mit.edu Abstract 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. 1 Introduction A central problem in language understanding is transforming raw text into structured representations. Learning-based approaches have dramatically increased the scope and robustness of this type of automatic language processing but they are typically dependent on large expert-annotated datasets which are costly to produce. In this paper we show how novice-generated free-text annotations available online can be leveraged to automatically infer document-level semantic properties. With the rapid increase of online content created by end users noisy free-text annotations have pros cons great nutritional value . combines it all an amazing product quick and friendly service cleanliness great .
