|Title: Improving Question Answering by Bridging Linguistic Structures and Statistical Learning|
|Speaker: Tomasz Jurczyk of Emory University|
|Date: 2017-11-02 at 4:00PM|
Question answering (QA) has lately gained lots of interest from both academic and industrial research. No matter the question, search engine users expect the machines to provide answers instantaneously, even without searching through relevant websites.\\ \\While a significant portion of these questions ask for concise and well known facts, more complex questions do exist and they often require dedicated approaches to provide robust and accurate systems.\\ \\This thesis explores linguistically-oriented approaches for both factoid and non-factoid question answering and applications to cross-genre tasks. The contributions include new annotation schemes for the question answering oriented corpora, extracting linguistic structures and performing matching, and early exploration of applications to conversation dialog tasks.
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