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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link=blue vlink=purple><div class=WordSection1><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#44546A'>Hi guys,<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#44546A'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#44546A'>By the way, every talk should include at least 2 quiz questions as before…<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#44546A'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#44546A'>Kenny<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#44546A'><o:p> </o:p></span></p><p class=MsoNormal><b><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>From:</span></b><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'> adapt-bounces@cs.sjtu.edu.cn [mailto:adapt-bounces@cs.sjtu.edu.cn] <b>On Behalf Of </b>zhiyi Luo<br><b>Sent:</b> Wednesday, September 12, 2018 11:47 AM<br><b>To:</b> adapt<br><b>Subject:</b> Re: [Adapt] [Seminar] Knowledge Base Question Answering via Encoding of Complex Query Structures<o:p></o:p></span></p><p class=MsoNormal><o:p> </o:p></p><div><div><div><p class=MsoNormal>Dear Adapters,<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>I will give the second half of the seminar, talking about our work "ExtRA: Extracting Prominent Review Aspects from Customer Feedback" which is accepted by EMNLP 2018 as a long paper. In this paper, we propose a framework, ExtRA, for extracting the most prominent aspects of a given product type from textual reviews. ExtRA extracts the aspect candidates from text reviews based on a data-driven approach, builds the aspect taxonomy by leveraging WordNet, then ranks the aspect candidates using the taxonomy, finally generates the expected prominent aspects for the product. Extensive experiments show that ExtRA is effective and achieves the state-of-the-art performance on a dataset consisting of different product types.<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>Best,<o:p></o:p></p></div><div><p class=MsoNormal>Zhiyi<o:p></o:p></p></div></div></div><p class=MsoNormal><o:p> </o:p></p><div><div><p class=MsoNormal>Luo Kangqi <<a href="mailto:luo.kangqi@qq.com">luo.kangqi@qq.com</a>> <span lang=ZH-CN style='font-family:宋体'>于</span>2018<span lang=ZH-CN style='font-family:宋体'>年</span>9<span lang=ZH-CN style='font-family:宋体'>月</span>12<span lang=ZH-CN style='font-family:宋体'>日周三</span><span lang=ZH-CN> </span><span lang=ZH-CN style='font-family:宋体'>上午</span>11:17<span lang=ZH-CN style='font-family:宋体'>写道:</span><o:p></o:p></p></div><blockquote style='border:none;border-left:solid #CCCCCC 1.0pt;padding:0in 0in 0in 6.0pt;margin-left:4.8pt;margin-right:0in'><div><p class=MsoNormal>Hi Adapters,<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>In our weekly seminar today, I'll give a talk on my paper "Knowledge Base Question Answering via Encoding of Complex Query Structures" accepted in EMNLP 2018.<o:p></o:p></p></div><div><p class=MsoNormal>The goal of KBQA is to answer natural language questions which ask existing facts of some specific entities in the knowledge base. We attempt to solve the KBQA problem in a more complex scenario, where multiple entities and relations are involved in one question. In this work, we encode the complex query structure of a question into a uniform vector representation, and thus successfully capture the interactions between individual semantic components within a complex <span class=m4280377671221476153fontstyle0>question. Experimental results on multiple KBQA datasets proved the effectiveness of our approach.</span><o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>Time: Sept 12nd, 5pm (Wed, <b>today</b>)<o:p></o:p></p></div><div><p class=MsoNormal>Venue: Room 3-517A <o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>See you there!<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>Best,<o:p></o:p></p></div><div><p class=MsoNormal>Kangqi<o:p></o:p></p></div><div><div><p class=MsoNormal><span style='font-size:9.0pt;font-family:"Arial Narrow","sans-serif";color:#909090'>------------------<o:p></o:p></span></p></div><div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Verdana","sans-serif";color:gray'>Kangqi Luo, PhD Candidate<o:p></o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Verdana","sans-serif";color:gray'>ADAPT Lab, Department of Computer Science<o:p></o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Verdana","sans-serif";color:gray'>SEIEE 3-341, Shanghai Jiao Tong University,<o:p></o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.5pt;font-family:"Verdana","sans-serif";color:gray'>No. 800 Dongchuan Road, Shanghai, China<o:p></o:p></span></p></div></div></div><div><p class=MsoNormal> <o:p></o:p></p></div><p class=MsoNormal>_______________________________________________<br>Adapt mailing list<br><a href="mailto:Adapt@cs.sjtu.edu.cn" target="_blank">Adapt@cs.sjtu.edu.cn</a><br><a href="http://cs.sjtu.edu.cn/mailman/listinfo/adapt" target="_blank">http://cs.sjtu.edu.cn/mailman/listinfo/adapt</a><o:p></o:p></p></blockquote></div></div></body></html>