Article ID: | iaor201112708 |
Volume: | 27 |
Issue: | 4 |
Start Page Number: | 681 |
End Page Number: | 701 |
Publication Date: | Nov 2011 |
Journal: | Computational Intelligence |
Authors: | Cohen K Bretonnel, Verspoor Karin, Johnson Helen L, Roeder Chris, Ogren Philip V, Baumgartner William A, White Elizabeth, Tipney Hannah, Hunter Lawrence |
Keywords: | event study, pattern recognition |
We approached the problems of event detection, argument identification, and
negation and speculation detection in the BioNLP’09 information extraction
challenge through concept recognition and analysis. Our methodology involved
using the OpenDMAP semantic parser with manually written rules. The original
OpenDMAP system was updated for this challenge with a broad ontology defined for
the events of interest, new linguistic patterns for those events, and
specialized coordination handling. We achieved state-of-the-art precision for
two of the three tasks, scoring the highest of 24 teams at precision of 71.81 on
Task 1 and the highest of 6 teams at precision of 70.97 on Task 2. We provide a
detailed analysis of the training data and show that a number of trigger words
were ambiguous as to event type, even when their arguments are constrained by
semantic class. The data is also shown to have a number of missing annotations.
Analysis of a sampling of the comparatively small number of false positives
returned by our system shows that major causes of this type of error were
failing to recognize second themes in two-theme events, failing to recognize
events when they were the arguments to other events, failure to recognize
nontheme arguments, and sentence segmentation errors. We show that specifically
handling coordination had a small but important impact on the overall
performance of the system. The OpenDMAP system and the rule set are available at