This paper presents a novel opinion mining research problem, which is called Contrastive Opinion Modeling (COM). Given any query topic and a set of text collections from multiple ...
We use a generative history-based model to predict the most likely derivation of a dependency parse. Our probabilistic model is based on Incremental Sigmoid Belief Networks, a rec...
The emergence of the world-wide-web has led to an increased interest in methods for searching for information. A key characteristic of many of the online document collections is t...
Information retrieval systems have to deal with uncertain knowledge and query results should reflect this uncertainty in some manner. However, Semantic Web ontologies are based on...
Methods for fusing document lists that were retrieved in response to a query often use retrieval scores (or ranks) of documents in the lists. We present a novel probabilistic fusi...