Oren Melamud, David McClosky, Siddharth Patwardhan, Mohit Bansal. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.

Do Kook Choe, David McClosky. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

Model combination techniques have consistently shown state-of-the-art performance across multiple tasks, including syntactic parsing.However, they dramatically increase runtime and can be difficult to employ in practice.We demonstrate that applying constituency model combination techniques to n-best lists instead of n different parsers results in significant parsing accuracy improvements.Parses are weighted by their probabilities and combined using an adapted version of Sagae and Lavie (2006).These accuracy gains come with marginal computational costs and are obtained on top of existing parsing techniques such as discriminative reranking and self-training, resulting in state-of-the-art accuracy: 92.6% on WSJ section 23.On out-of-domain corpora, accuracy is improved by 0.4% on average.We empirically confirm that six well-known n-best parsers benefit from the proposed methods across six domains.

BACKGROUND: We explore techniques for performing model combination between the UMass and Stanford biomedical event extraction systems. Both sub-components address event extraction as a structured prediction problem, and use dual decomposition (UMass) and parsing algorithms (Stanford) to find the best scoring event structure. Our primary focus is on stacking where the predictions from the Stanford system are used as features in the UMass system. For comparison, we look at simpler model combination techniques such as intersection and union which require only the outputs from each system and combine them directly. RESULTS: First, we find that stacking substantially improves performance while intersection and union provide no significant benefits. Second, we investigate the graph properties of event structures and their impact on the combination of our systems. Finally, we trace the origins of events proposed by the stacked model to determine the role each system plays in different components of the output. We learn that, while stacking can propose novel event structures not seen in either base model, these events have extremely low precision. Removing these novel events improves our already state-of-the-art F1 to 56.6% on the test set of Genia (Task 1). Overall, the combined system formed via stacking ("FAUST") performed well in the BioNLP 2011 shared task. The FAUST system obtained 1st place in three out of four tasks: 1st place in Genia Task 1 (56.0% F1) and Task 2 (53.9%), 2nd place in the Epigenetics and Post-translational Modifications track (35.0%), and 1st place in the Infectious Diseases track (55.6%). CONCLUSION: We present a state-of-the-art event extraction system that relies on the strengths of structured prediction and model combination through stacking. Akin to results on other tasks, stacking outperforms intersection and union and leads to very strong results. The utility of model combination hinges on complementary views of the data, and we show that our sub-systems capture different graph properties of event structures. Finally, by removing low precision novel events, we show that performance from stacking can be further improved.

Statistical parsers trained and tested on the Penn Wall Street Journal (WSJ) treebank have shown vast improvements over the last 10 years. Much of this improvement, however, is based upon an ever-increasing number of features to be trained on (typically) the WSJ treebank data. This has led to concern that such parsers may be too finely tuned to this corpus at the expense of portability to other genres. Such worries have merit. The standard "Charniak parser" checks in at a labeled precision-recall f-measure of 89.7% on the Penn WSJ test set, but only 82.9% on the test set from the Brown treebank corpus.This paper should allay these fears. In particular, we show that the reranking parser described in Charniak and Johnson (2005) improves performance of the parser on Brown to 85.2%. Furthermore, use of the self-training techniques described in (McClosky et al., 2006) raise this to 87.8% (an error reduction of 28%) again without any use of labeled Brown data. This is remarkable since training the parser and reranker on labeled Brown data achieves only 88.4%.