We develop a general method to match unstructured text reviews to a structured list of objects. For this, we propose a language model for generating reviews that incorporates a description of objects and a generic review language model. This mixture model gives us a principled method to find, given a review, the object most likely to be the topic of the review. Extensive experiments and analysis on reviews from Yelp show that our language model-based method vastly outperforms traditional tfidf-based methods.
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