|Title: Question Answering with User Generated Content|
|Speaker: Denis Savenkov of Emory University|
|Contact: Denis Savenkov, email@example.com|
|Date: 2017-04-13 at 4:00PM|
Modern search engines have made dramatic progress in answering many user questions, especially about facts, such as those that might be retrieved or directly inferred from a knowledge base. However, many other more complex factual, opinion or advice questions, are still largely beyond the competence of computer systems. For such information needs users still have to dig into the "10 blue links" of search results and extract relevant information. As conversational agents become more popular, question answering (QA) systems are increasingly expected to handle such complex questions and provide users with helpful and concise information. In my dissertation I develop new methods to improve the performance of question answering systems for a diverse set of user information needs using various types of user-generated content, such as text documents, community question answering archives, knowledge bases, direct human contributions, and explore the opportunities of conversational settings for information seeking scenarios.
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