Event Recognition Based on Deep Learning in Chinese Texts

Zhang, Yajun; Liu, Zongtian; Zhou, Wen

doi:10.1371/journal.pone.0160147

Cited by 14 publications

(8 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, the researcher proposed some novel models to solve a different problem in event extraction related to our work. Zhang et al [23] transformed the event recognition problem into semantic feature classification and proposed a deep belief network model to identify the triggers. Chen et al [8] proposed a dynamic multi-pool convolutional neural network to capture the sentence-level information for event extraction.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Capturing semantic features to improve Chinese event detection

Liu

Ouyang

2021

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

Current Chinese event detection methods commonly use word embedding to capture semantic representation, but these methods find it difficult to capture the dependence relationship between the trigger words and other words in the same sentence. Based on the simple evaluation, it is known that a dependency parser can effectively capture dependency relationships and improve the accuracy of event categorisation. This study proposes a novel architecture that models a hybrid representation to summarise semantic and structural information from both characters and words. This model can capture rich semantic features for the event detection task by incorporating the semantic representation generated from the dependency parser. The authors evaluate different models on kbp 2017 corpus. The experimental results show that the proposed method can significantly improve performance in Chinese event detection.

show abstract

Section: Deep Learning Methodsmentioning

confidence: 99%

Capturing semantic features to improve Chinese event detection

Liu

Ouyang

2021

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

“…Zhang [12] proposed a deep belief network model with mixed supervision to identify the trigger words.…”

Section: Machine Learningmentioning

confidence: 99%

Chinese Event Extraction Based on Attention and Semantic Features: A Bidirectional Circular Neural Network

Zhang

2018

Future Internet

View full text Add to dashboard Cite

Chinese event extraction uses word embedding to capture similarity, but suffers when handling previously unseen or rare words. From the test, we know that characters may provide some information that we cannot obtain in words, so we propose a novel architecture for combining word representations: character–word embedding based on attention and semantic features. By using an attention mechanism, our method is able to dynamically decide how much information to use from word or character level embedding. With the semantic feature, we can obtain some more information about a word from the sentence. We evaluate different methods on the CEC Corpus, and this method is found to improve performance.

show abstract

“…The proposed VBN framework was applied for modeling word dependencies in text and it can achieve over 30% improvement in accuracy on real-world scenarios compared to the baselines. A Chinese Emergency Event Recognition Model (CEERM) [41] based on deep learning achieves excellent recognition performance with a maximum F-measure value of 85.17%.…”

Section: Deep Learningmentioning

confidence: 99%

Comparison of Conventional Method of Fault Determination with Data-Driven Approach for Ball Bearings in a Wind Turbine Gearbox

Balavignesh¹

2018

IJPE

View full text Add to dashboard Cite

Android OS maintains its dominance in smart terminal markets, which brings growing threats of malicious applications (apps). The research on Android malware detection has attracted attention from both academia and industry. How to improve the malware detection performance, what classifiers should be selected, and what features should be employed are all critical issues that need to be solved. Convolutional Neural Networks (CNN) is a typical deep learning technique that has achieved great performance in image and speech recognitions. In this work, we present an Android malware detection framework Andro_MD that can train and classify samples with a deep learning technique. The framework includes dataset construction and feature preprocessing, training and classification by CNN, and evaluation by experiments. First, an Android app dataset is constructed with 21,000 samples collected from third-party markets and 34,570 features of 7 categories. Second, we employ sequential and parallel models to train the extracted features and classify the malware apps. Finally, extensive experimental results show the effectiveness and feasibility of the proposed method. Comparisons with similar work and traditional classifiers show that Andro_MD has a better performance on malware detection, and its accuracy can achieve 99.25% with a FPR of 0.53%. The "request permissions" and "used permissions" feature categories have better performances with limited dimensions.

show abstract

Event Recognition Based on Deep Learning in Chinese Texts

Cited by 14 publications

References 28 publications

Capturing semantic features to improve Chinese event detection

Capturing semantic features to improve Chinese event detection

Chinese Event Extraction Based on Attention and Semantic Features: A Bidirectional Circular Neural Network

Comparison of Conventional Method of Fault Determination with Data-Driven Approach for Ball Bearings in a Wind Turbine Gearbox

Contact Info

Product

Resources

About