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--></style></head><body bgcolor=white lang=ZH-CN link="#0563C1" vlink="#954F72"><div class=WordSection1><p class=MsoNormal><span lang=EN-US style='font-size:10.5pt'>Hi Adapters,<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:10.5pt'> This afternoon I’ll give a talk about an interesting approach to information extraction.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:10.5pt'> When facing an IE task, a common issue is that “ambiguous” expression occurs. For example, when extracting number of people from “A couple and their four children had a good vacation”, the extractor may return an incorrect answer because “couple” stands for two people. Even a large annotated model may not cover such cases.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:10.5pt'> The paper gives an approach to solve the problem by using external information from web and reconcile to a more accurate output, not enlarging the label process or adding ambiguous words handling.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:10.5pt'> Models in this paper are a basic extractor based on CRF and a simple reinforcement learning model. The overall process lays on a markov decision process.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:10.5pt'> See you this afternoon!<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:10.5pt'>Best wishes,<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:10.5pt'>Gavin<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:10.5pt'><o:p> </o:p></span></p></div></body></html>