Paper: Relieving the Computational Bottleneck: Joint Inference for Event Extraction with High-Dimensional Features

ACL ID D14-1090
Title Relieving the Computational Bottleneck: Joint Inference for Event Extraction with High-Dimensional Features
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

Several state-of-the-art event extraction sys- tems employ models based on Support Vec- tor Machines (SVMs) in a pipeline architec- ture, which fails to exploit the joint depen- dencies that typically exist among events and arguments. While there have been at- tempts to overcome this limitation using Markov Logic Networks (MLNs), it re- mains challenging to perform joint infer- ence in MLNs when the model encodes many high-dimensional sophisticated fea- tures such as those essential for event ex- traction. In this paper, we propose a new model for event extraction that combines the power of MLNs and SVMs, dwarfing their limitations. The key idea is to reli- ably learn and process high-dimensional features using SVMs; encode the output of SVMs as low-dimensional, soft formu- las in MLNs; an...