Answering Deep Queries Specified in Natural Language with Respect to a Frame Based Knowledge Base and Developing Related Natural Language Understanding Components
|Abstract||Question Answering has been under active research for decades, but it has recently taken the spotlight following IBM Watson's success in Jeopardy! and digital assistants such as Apple's Siri, Google Now, and Microsoft Cortana through every smart-phone and browser. However, most of the research in Question Answering aims at factual questions rather than deep ones such as ``How'' and ``Why'' questions.
In this dissertation, I suggest a different approach in tackling this problem. We believe that the answers of deep questions need to be formally defined before found.
Because these answers must be defined based on something, it is better to be more structural in natural language text; I define Knowledge Description G... (more)
|Contributor||Vo, Nguyen Ha (Author) / Baral, Chitta (Advisor) / Lee, Joohyung (Committee member) / VanLehn, Kurt (Committee member) / Tran, Son Cao (Committee member) / Arizona State University (Publisher)|
|Subject||Artificial intelligence / Computer science / deep question / How and Why / knowledge representation / natural language processing / natural language understanding / question answering|
|Reuse Permissions||All Rights Reserved|
|Note||Doctoral Dissertation Computer Science 2015|
|Collaborating Institutions||Graduate College / ASU Library|
|Additional Formats||MODS / OAI Dublin Core / RIS|