scholarly journals SEML: A Semi-Supervised Multi-Task Learning Framework for Aspect-Based Sentiment Analysis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 189287-189297
Author(s):  
Ning Li ◽  
Chi-Yin Chow ◽  
Jia-Dong Zhang
Keyword(s):  
Sentiment Analysis ◽  
Learning Framework ◽  
Task Learning

scholarly journals Multi-Task Learning for Sentiment Analysis with Hard-Sharing and Task Recognition Mechanisms

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 207
Author(s):  
Jian Zhang ◽  
Ke Yan ◽  
Yuchang Mo
Keyword(s):  
Sentiment Analysis ◽  
Feature Space ◽  
Classification Performance ◽  
Recognition Mechanism ◽  
Shared Feature ◽  
Learning Framework ◽  
Task Learning ◽  
Multiple Tasks ◽  
Task Recognition ◽  
And Task

In the era of big data, multi-task learning has become one of the crucial technologies for sentiment analysis and classification. Most of the existing multi-task learning models for sentiment analysis are developed based on the soft-sharing mechanism that has less interference between different tasks than the hard-sharing mechanism. However, there are also fewer essential features that the model can extract with the soft-sharing method, resulting in unsatisfactory classification performance. In this paper, we propose a multi-task learning framework based on a hard-sharing mechanism for sentiment analysis in various fields. The hard-sharing mechanism is achieved by a shared layer to build the interrelationship among multiple tasks. Then, we design a task recognition mechanism to reduce the interference of the hard-shared feature space and also to enhance the correlation between multiple tasks. Experiments on two real-world sentiment classification datasets show that our approach achieves the best results and improves the classification accuracy over the existing methods significantly. The task recognition training process enables a unique representation of the features of different tasks in the shared feature space, providing a new solution reducing interference in the shared feature space for sentiment analysis.

scholarly journals Multi-task learning based Encoder-Decoder: A comprehensive detection and diagnosis system for multi-sensor data

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110131
Author(s):  
Junfeng Wu ◽  
Li Yao ◽  
Bin Liu ◽  
Zheyuan Ding ◽  
Lei Zhang
Keyword(s):  
Anomaly Detection ◽  
Event Detection ◽  
Large Scale ◽  
Multivariate Time Series ◽  
Sensor Data ◽  
Unified Framework ◽  
Diagnosis System ◽  
Learning Framework ◽  
Task Learning ◽  
Detection And Diagnosis

As more and more sensor data have been collected, automated detection, and diagnosis systems are urgently needed to lessen the increasing monitoring burden and reduce the risk of system faults. A plethora of researches have been done on anomaly detection, event detection, anomaly diagnosis respectively. However, none of current approaches can explore all these respects in one unified framework. In this work, a Multi-Task Learning based Encoder-Decoder (MTLED) which can simultaneously detect anomalies, diagnose anomalies, and detect events is proposed. In MTLED, feature matrix is introduced so that features are extracted for each time point and point-wise anomaly detection can be realized in an end-to-end way. Anomaly diagnosis and event detection share the same feature matrix with anomaly detection in the multi-task learning framework and also provide important information for system monitoring. To train such a comprehensive detection and diagnosis system, a large-scale multivariate time series dataset which contains anomalies of multiple types is generated with simulation tools. Extensive experiments on the synthetic dataset verify the effectiveness of MTLED and its multi-task learning framework, and the evaluation on a real-world dataset demonstrates that MTLED can be used in other application scenarios through transfer learning.

Semi-Supervised Aspect-Based Sentiment Analysis for Case-Related Microblog Reviews Using Case Knowledge Graph Embedding

2021 ◽  
pp. 2050012
Author(s):  
Peilian Zhao ◽  
Cunli Mao ◽  
Zhengtao Yu
Keyword(s):  
Sentiment Analysis ◽  
Domain Knowledge ◽  
Opinion Mining ◽  
Data Augmentation ◽  
Training Data ◽  
Knowledge Graph ◽  
Fine Grained ◽  
Learning Framework ◽  
Proposed Model ◽  
Real World Applications

Aspect-Based Sentiment Analysis (ABSA), a fine-grained task of opinion mining, which aims to extract sentiment of specific target from text, is an important task in many real-world applications, especially in the legal field. Therefore, in this paper, we study the problem of limitation of labeled training data required and ignorance of in-domain knowledge representation for End-to-End Aspect-Based Sentiment Analysis (E2E-ABSA) in legal field. We proposed a new method under deep learning framework, named Semi-ETEKGs, which applied E2E framework using knowledge graph (KG) embedding in legal field after data augmentation (DA). Specifically, we pre-trained the BERT embedding and in-domain KG embedding for unlabeled data and labeled data with case elements after DA, and then we put two embeddings into the E2E framework to classify the polarity of target-entity. Finally, we built a case-related dataset based on a popular benchmark for ABSA to prove the efficiency of Semi-ETEKGs, and experiments on case-related dataset from microblog comments show that our proposed model outperforms the other compared methods significantly.

scholarly journals Learning to Incorporate Structure Knowledge for Image Inpainting

2020 ◽  
Author(s):  
Jie Yang ◽  
Zhiquan Qi ◽  
Yong Shi
Keyword(s):  
Structure Learning ◽  
State Of The Art ◽  
Image Inpainting ◽  
Image Completion ◽  
Image Structure ◽  
Learning Framework ◽  
Task Learning ◽  
Pyramid Structure ◽  
Benchmark Datasets ◽  
Structure Knowledge

This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works. The primary idea is to train a shared generator to simultaneously complete the corrupted image and corresponding structures --- edge and gradient, thus implicitly encouraging the generator to exploit relevant structure knowledge while inpainting. In the meantime, we also introduce a structure embedding scheme to explicitly embed the learned structure features into the inpainting process, thus to provide possible preconditions for image completion. Specifically, a novel pyramid structure loss is proposed to supervise structure learning and embedding. Moreover, an attention mechanism is developed to further exploit the recurrent structures and patterns in the image to refine the generated structures and contents. Through multi-task learning, structure embedding besides with attention, our framework takes advantage of the structure knowledge and outperforms several state-of-the-art methods on benchmark datasets quantitatively and qualitatively.

scholarly journals Learning to Incorporate Structure Knowledge for Image Inpainting

2020 ◽  
Vol 34 (07) ◽  
pp. 12605-12612 ◽  
Author(s):  
Jie Yang ◽  
Zhiquan Qi ◽  
Yong Shi
Keyword(s):  
Structure Learning ◽  
State Of The Art ◽  
Image Inpainting ◽  
Image Completion ◽  
Image Structure ◽  
Learning Framework ◽  
Task Learning ◽  
Pyramid Structure ◽  
Benchmark Datasets ◽  
Structure Knowledge

This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works. The primary idea is to train a shared generator to simultaneously complete the corrupted image and corresponding structures — edge and gradient, thus implicitly encouraging the generator to exploit relevant structure knowledge while inpainting. In the meantime, we also introduce a structure embedding scheme to explicitly embed the learned structure features into the inpainting process, thus to provide possible preconditions for image completion. Specifically, a novel pyramid structure loss is proposed to supervise structure learning and embedding. Moreover, an attention mechanism is developed to further exploit the recurrent structures and patterns in the image to refine the generated structures and contents. Through multi-task learning, structure embedding besides with attention, our framework takes advantage of the structure knowledge and outperforms several state-of-the-art methods on benchmark datasets quantitatively and qualitatively.

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