Joint Event Extraction via Recurrent Neural Networks

Abstract

Event extraction is a particularly challenging problem in information extraction. The stateof-the-art models for this problem have either applied convolutional neural networks in a pipelined framework (Chen et al., 2015) or followed the joint architecture via structured prediction with rich local and global features (Li et al., 2013). The former is able to learn hidden feature representations automatically from data based on the continuous and generalized representations of words. The latter, on the other hand, is capable of mitigating the error propagation problem of the pipelined approach and exploiting the inter-dependencies between event triggers and argument roles via discrete structures. In this work, we propose to do event extraction in a joint framework with bidirectional recurrent neural networks, thereby benefiting from the advantages of the two models as well as addressing issues inherent in the existing approaches. We systematically investigate different memory features for the joint model and demonstrate that the proposed model achieves the state-of-the-art performance on the ACE 2005 dataset.

Boosting Information Extraction Systems with Character-level Neural Networks and Free Noisy Supervision

“…Our extraction system has three advantages over earlier work on information extraction with deep neural networks (Socher et al 2013;Nguyen, Cho, and Grishman 2016;Nguyen and Grishman 2015;Schuster and Paliwal 1997;Zhou and Xu 2015;Chiu and Nichols 2015;Ballesteros, Dyer, and Smith 2015;Miwa and Bansal 2016):…”

Exploiting the Matching Information in the Support Set for Few Shot Event Classification

“…Early studies in event classification mainly focus on designing linguistic features [1,9,12] for statistical models. Due to the development of deep learning, many advanced network architectures have been investigated to advance the event classification accuracy [5,13,[17][18][19]21,22]. However, none of them investigates the few-shot learning problem for EC as we do in this work.…”

“…The second approach, on the other hand, focuses on developing deep neural network models (e.g., convolutional neural network (CNN) and recurrent neural network (RNN)) to automatically learn effective features from large scale datasets [5,13]. Due to the development of the deep learning models, the performance for EC has been improved significantly [14,16,17,19,23].…”

“…The current EC models mainly employ the traditional supervised learning setting [17,19] where the set of event types for classification has been predetermined. However, once a model is trained on the datasets with the given set of event types, it is unable to detect event mentions of unseen event types.…”

Multilingual Named Entity Recognition Using Pretrained Embeddings, Attention Mechanism and NCRF

“…From ULMFiT work we took part which is related to the final decoding for classification (Pooling Classifier) without proposed language model (Howard and Ruder, 2018). Secondly we model the task of NER as a joint sequence labeling and classification task following other joint architec-tures (Liu and Lane, 2016;Nguyen et al, 2016).…”