TY - GEN
T1 - Using Prediction from Sentential Scope to Build a Pseudo Co-Testing Learner for Event Extraction
AU - Liao, Shasha
AU - Grishman, Ralph
N1 - Funding Information:
Supported by the Intelligence Advanced Research Projects Activity (IARPA) via Air Force Research Laboratory (AFRL) contract number FA8650-10-C-7058. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, AFRL, or the U.S. Government.
Publisher Copyright:
© 2011 AFNLP
PY - 2011
Y1 - 2011
N2 - 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 pseudo-contention-points which are very informative, and can speed up the AL process. Moreover, we incorporate multiple selection criteria into the pseudo co-testing, 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%.
AB - 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 pseudo-contention-points which are very informative, and can speed up the AL process. Moreover, we incorporate multiple selection criteria into the pseudo co-testing, 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%.
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M3 - Conference contribution
AN - SCOPUS:84876786846
T3 - IJCNLP 2011 - Proceedings of the 5th International Joint Conference on Natural Language Processing
SP - 714
EP - 722
BT - IJCNLP 2011 - Proceedings of the 5th International Joint Conference on Natural Language Processing
A2 - Wang, Haifeng
A2 - Yarowsky, David
PB - Association for Computational Linguistics (ACL)
T2 - 5th International Joint Conference on Natural Language Processing, IJCNLP 2011
Y2 - 8 November 2011 through 13 November 2011
ER -