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Neural Information Extraction With Memory

Abstract:

Neural Networks (NN) have been applied successfully to many Information Extraction (IE) tasks recently. However, most of the current models are designed for a separate task of the IE pipeline, focusing only on the local information specific to the task. Such local models are not able to capture the global information or the long range inter-dependencies between multiple prediction stages, that are necessary for many IE problems.

In this talk, we present our recent research on memory augmented networks to address such limitations of the local NN models. In particular, we introduce memory tensors to accumulate the prediction information during the course of the local stages, and provide such global memory as additional evidence for the local predictions of IE. Our experiments on event extraction and entity linking demonstrate that the memory augmented networks outperform the traditional local NN models and feature-based approaches for such problems.

Bio:

Thien Huu Nguyen is a fifth-year Ph.D. student in the Computer Science Department at New York University (NYU). His Ph.D. research centers around the development of Deep Learning models for Information Extraction of Natural Language Processing, including Relation Extraction, Event Extraction, Mention Detection, and Slot Filling. His research advisors at NYU are Professor Ralph Grishman and Professor Kyunghyun Cho.

Thien Huu Nguyen was a research intern at the IBM T.J. Watson Research Center (Yorktown Heights, New York) in the summers of 2015 and 2016, where he developed new neural network models for Mention Detection and Entity Linking. Thien is a recipient of the IBM Ph.D. fellowship (2016-2017) and he is expected to graduate in May 2017.