Using Prediction from Sentential Scope to Build a Pseudo Co-Testing Learner for Event Extraction
Shasha Liao, Ralph Grishman · 2011
Event extraction involves the identification of instances of a type of event, along with their attributes and participants. Developing a training corpus by annotating events in text is very labor intensive, and so selecting informative instances to annotate can save a great deal of manual work. We present an active learning (AL) strategy, pseudo co-testing, based on one view from a classifier aiming to solve the original problem of event extraction, and another view from a classifier aiming to solve a coarser granularity task. As the second classifier can provide more graded matching from a wider scope, we can build a set of pseudocontention-points which are very informative, and can speed up the AL process. Moreover, we incorporate multiple selection criteria into the pseudo cotesting, seeking training examples that are informative, representative, and varied. Experiments show that pseudo co-testing can reduce annotation labor by 81%; incorporating multiple selection criteria reduces the labor by a further 7%. 1