Event Arguments Extraction via Dilate Gated Convolutional Neural Network With Enhanced Local Features

Kan, Zhigang; Qiao, Linbo; Yang, Sen; Liu, Feng; Huang, Feng

doi:10.1109/access.2020.3004378

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…In order to solve the challenge, this paper introduces the approach of combining the NSP task in BERT to capture the relationship between the event trigger and the original sentence as mentioned in Ref. [10]. The NSP task in the BERT model is to predict whether two sentences are preceding or following sentences, and the similarity between two sentences text1 and text2 is calculated by the text similarity task; if they are similar, then it is considered that text2 is the next sentence of text1, and vice versa.…”

Section: Event Argument Extraction Decodermentioning

confidence: 99%

Combining NSP and NER for public opinion event extraction model

Zhang

Wei²,

Yan³

2022

Front. Phys.

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Event extraction in the field of public opinion aims to extract important event arguments and their corresponding roles from the moment-to-moment generated opinion reports. Most of the existing research methods divide the task into three subtasks: event trigger extraction, event type detection, and event argument extraction. Despite the remarkable achievements of the event argument extraction paradigm combining part-of-speech (POS) and event trigger features, the performance of POS features in combinatorial event argument extraction tasks is struggling due to its inherent semantic diversity in Chinese. In addition, previous research work ignored the deep semantic interaction between event trigger and text. To address the aforementioned problems, this paper proposes an opinion event extraction model (NN-EE) combining NSP and NER, which alleviates the lack of performance of combinatorial event argument extraction by introducing NER technology. Meanwhile, the event trigger features are incorporated into the NSP mechanism of the pre-trained language model BERT to prompt the model to learn the deep semantic interaction between the event trigger and original text. The results of the self-constructed food opinion report dataset (FD-OR) in this paper show that the NN-EE model achieves optimal performance.

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Section: Event Argument Extraction Decodermentioning

confidence: 99%

Combining NSP and NER for public opinion event extraction model

Zhang

Wei²,

Yan³

2022

Front. Phys.

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“…To solve the problem of the need to design different neural networks for the feature representation of the above methods, PLMEE [17] uses the pre-trained language model for the event extraction task for the first time. EE-DGCNN [18] adopts multi-layer dilate gated CNN to reduce the number of PLMEE parameters. These methods have a good performance on English data sets.…”

Section: Event Detection (Ed)mentioning

confidence: 99%

Glyfn: A Glyph-Aware Fusion Network for Distributed Chinese Event Detection

Zhai¹,

Kan²,

Feng³

et al. 2021

Computer Science &Amp; Information Technology (CS &Amp; IT)

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Recently, Chinese event detection has attracted more and more attention. As a special kind of hieroglyphics, Chinese glyphs are semantically useful but still unexplored in this task. In this paper, we propose a novel Glyph-Aware Fusion Network, named GlyFN. It introduces the glyphs' information into the pre-trained language model representation. To obtain a better representation, we design a Vector Linear Fusion mechanism to fuse them. Specifically, it first utilizes a max-pooling to capture salient information. Then, we use the linear operation of vectors to retain unique information. Moreover, for large-scale unstructured text, we distribute the data into different clusters parallelly. Finally, we conduct extensive experiments on ACE2005 and large-scale data. Experimental results show that GlyFN obtains increases of 7.48(10.18%) and 6.17(8.7%) in the F1-score for trigger identification and classification over the state-of-the-art methods, respectively. Furthermore, the event detection task for large-scale unstructured text can be efficiently accomplished through distribution.

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“…In order to reduce the computation and footprint, we design a novel CNN model based on multilayer Dilate Gated Convolutional Neural Network (DGCNN). EE-DGCNN [34] demonstrated the potential of the DGCNN for event detection tasks and FPGA implementations. DGCNN can reduce computation complexity while obtaining long-term dependencies owing to the use of the dilated convolution.…”

Section: Introductionmentioning

confidence: 98%

Accelerating Event Detection with DGCNN and FPGAs

et al. 2020

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Recently, Deep Neural Networks (DNNs) have been widely used in natural language processing. However, DNNs are often computation-intensive and memory-expensive. Therefore, deploying DNNs in the real world is very difficult. In order to solve this problem, we proposed a network model based on the dilate gated convolutional neural network, which is very hardware-friendly. We further expanded the word representations and depth of the network to improve the performance of the model. We replaced the Sigmoid function to make it more friendly for hardware computation without loss, and we quantized the network weights and activations to compress the network size. We then proposed the first FPGA (Field Programmable Gate Array)-based event detection accelerator based on the proposed model. The accelerator significantly reduced the latency with the fully pipelined architecture. We implemented the accelerator on the Xilinx XCKU115 FPGA. The experimental results show that our model obtains the highest F1-score of 84.6% in the ACE 2005 corpus. Meanwhile, the accelerator achieved 95.2 giga operations (GOP)/s and 13.4 GOPS/W in performance and energy efficiency, which is 17/158 times higher than the Graphics Processing Unit (GPU).

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Event Arguments Extraction via Dilate Gated Convolutional Neural Network With Enhanced Local Features

Cited by 7 publications

References 11 publications

Combining NSP and NER for public opinion event extraction model

Combining NSP and NER for public opinion event extraction model

Glyfn: A Glyph-Aware Fusion Network for Distributed Chinese Event Detection

Accelerating Event Detection with DGCNN and FPGAs

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