We present a novel approach to discovering relations and their instantiations from a collection of documents in a single domain. Our approach learns relation types by exploiting meta-constraints that characterize the general qualities of a good relation in any domain. These constraints state that instances of a single relation should exhibit regularities at multiple levels of linguistic structure, including lexicography, syntax, and document-level context. We capture these regularities via the structure of our probabilistic model as well as a set of declaratively-specified constraints enforced during posterior inference. Across two domains our approach successfully recovers hidden relation structure, comparable to or outperforming previous state-of-the-art approaches. Furthermore, we find that a small set of constraints is applicable across the domains, and that using domain-specific constraints can further improve performance. 1 1

GenerativetopicmodelssuchasLDAarelimited by their inability to utilize nontrivial input features to enhance their performance, and many topic models assume that topic assignmentsofdifferent wordsareconditionally independent. Some work exists to address the second limitation but no work exists to address both. This paper presents a conditional topic random field (CTRF) model, which can use arbitrary nonlocal features about words and documents and incorporate the Markov dependency between topic assignments of neighboring words. We develop an efficient variational inference algorithm that scales linearly in terms of topic numbers, and a maximum likelihood estimation (MLE) procedure for parameter estimation. For the supervised version of CTRF, we alsodevelopan arguablymorediscriminative max-margin learning method. We evaluate CTRF on real review rating data and demonstrate the advantages of CTRF over generative competitors, and we show the advantages of max-margin learning over MLE. 1.

In this paper, we investigate how modeling content structure can benefit text analysis applications such as extractive summarization and sentiment analysis. This follows the linguistic intuition that rich contextual information should be useful in these tasks. We present a framework which combines a supervised text analysis application with the induction of latent content structure. Both of these elements are learned jointly using the EM algorithm. The induced content structure is learned from a large unannotated corpus and biased by the underlying text analysis task. We demonstrate that exploiting content structure yields significant improvements over approaches that rely only on local context. 1 1