Towards 'interactive' active learning in multi-view feature sets for information extraction

Abstract

Research in multi-view active learning has typically focused on algorithms for selecting the next example to label. This is often at the cost of lengthy wait-times for the user between each query iteration. We deal with a real-world information extraction task, extracting attribute-value pairs from product descriptions, where the learning system needs to be interactive and the users time needs to be used efficiently. The first step uses coEM with naive Bayes as the semi-supervised algorithm. This paper focuses on the second step which is an interactive active learning phase. We present an approximation to coEM with naive Bayes that can incorporate user feedback almost instantly and can use any sample-selection strategy for active learning. Our experimental results show high levels of accuracy while being orders of magnitude faster than using the standard coEM with naive Bayes, making our IE system practical by optimizing user time. © Springer-Verlag Berlin Heidelberg 2007.