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Ivan Titov |
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User generated content represents a unique source of information in which user interface tools have facilitated the creation of an abundance of labeled content, e.g., topics in blogs, numerical product and service ratings in user reviews, and helpfulness rankings in online discussion forums. Many previous studies on user generated content have attempted to predict these labels automatically from the associated text. However, these annotations are often present in the data already, which opens another interesting line of research: designing models leveraging these labelings to improve a wide variety of applications. In this talk I will be considering the sentiment summarization problem. I will present statistical models which exploit user generated aspect ratings to discover corresponding topics and are therefore able to extract fragments of text discussing these aspects without the need of annotated data. Joint work with Ryan McDonald. |
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Official inquiries about AIIS should
be directed to Alexandre Klementiev (klementi AT uiuc DOT edu) |
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