A Hybrid Method Based on Semi-Supervised Learning for Relation Extraction in Chinese EMRs (Preprint)

2021 ◽  
Author(s):  
ChunMing Yang
Keyword(s):  
Supervised Learning ◽  
Learning Algorithm ◽  
Medical Knowledge ◽  
Relation Extraction ◽  
Small Scale ◽  
Semantic Features ◽  
Training Process ◽  
Network Layers ◽  
Relation Prediction ◽  
The Cost

BACKGROUND Extracting relations between the entities from Chinese electronic medical records(EMRs) is the key to automatically constructing medical knowledge graphs. Due to the less available labeled corpus, most of the current researches are based on shallow networks, which cannot fully capture the complex semantic features in the text of Chinese EMRs. OBJECTIVE In this study, a hybrid deep learning method based on semi-supervised learning is proposed to extract the entity relations from small-scale complex Chinese EMRs. METHODS The semantic features of sentences are extracted by residual network (ResNet) and the long dependent information is captured by bidirectional GRU (Gated Recurrent Unit). Then the attention mechanism is used to assign weights to the extracted features respectively, and the output of the two attention mechanisms is integrated for relation prediction. We adjusted the training process with manually annotated small-scale relational corpus and bootstrapping semi-supervised learning algorithm, and continuously expanded the datasets during the training process. RESULTS The experimental results show that the best F1-score of the proposed method on the overall relation categories reaches 89.78%, which is 13.07% higher than the baseline CNN model. The F1-score on DAP, SAP, SNAP, TeRD, TeAP, TeCP, TeRS, TeAS, TrAD, TrRD and TrAP 11 relation categories reaches 80.95%, 93.91%, 92.96%, 88.43%, 86.54%, 85.58%, 87.96%, 94.74%, 93.01%, 87.58% and 95.48%, respectively. CONCLUSIONS The hybrid neural network method strengthens the feature transfer and reuse between different network layers and reduces the cost of manual tagging relations. The results demonstrate that our proposed method is effective for the relation extraction in Chinese EMRs.

Research on the Entity Relation Extraction of Field Based on Semi-Supervised

2011 ◽  
Vol 225-226 ◽  
pp. 1292-1300
Author(s):  
Jian Yi Guo ◽  
Jun Zhao ◽  
Zheng Tao Yu ◽  
Lei Su ◽  
Yan Tuan Xian ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Information Entropy ◽  
Rate Increase ◽  
Relation Extraction ◽  
Classification Performance ◽  
Training Data ◽  
Small Scale ◽  
Result Show ◽  
Entity Relation Extraction ◽  
Precision Rate

Aim at the problem of supervised learning needing much labeled data for training, this paper proposes a new method based on Semi-Supervised learning. Firstly, to construct a classifier of certain accuracy, small-scale training data was used.Secondly, with the self-expanding idea, we applied the method of information entropy to select some new instances of higher credibility from candidate instances, which were to be predicted by the classifier. Finally, with the expansion of training data, training classifier re-iteratively, classification performance tended to be stable iteration termination, which achieved the entity relation extraction of tourism field by semi-supervised learning. The experiments result show that the new classifier which applies information entropy to iteratively expand training data to be trained makes the precision rate increase by 7% and the F-score increase by 15%.

scholarly journals Distant Supervision for Relation Extraction with Sentence Selection and Interaction Representation

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Tiantian Chen ◽  
Nianbin Wang ◽  
Hongbin Wang ◽  
Haomin Zhan
Keyword(s):  
Large Scale ◽  
Semantic Information ◽  
State Of The Art ◽  
Relation Extraction ◽  
Semantic Features ◽  
Distant Supervision ◽  
Word Level ◽  
Proposed Model ◽  
Relation Prediction ◽  
Better Than

Distant supervision (DS) has been widely used for relation extraction (RE), which automatically generates large-scale labeled data. However, there is a wrong labeling problem, which affects the performance of RE. Besides, the existing method suffers from the lack of useful semantic features for some positive training instances. To address the above problems, we propose a novel RE model with sentence selection and interaction representation for distantly supervised RE. First, we propose a pattern method based on the relation trigger words as a sentence selector to filter out noisy sentences to alleviate the wrong labeling problem. After clean instances are obtained, we propose the interaction representation using the word-level attention mechanism-based entity pairs to dynamically increase the weights of the words related to entity pairs, which can provide more useful semantic information for relation prediction. The proposed model outperforms the strongest baseline by 2.61 in F1-score on a widely used dataset, which proves that our model performs significantly better than the state-of-the-art RE systems.

The Turnout Abnormality Diagnosis Based on Semi-Supervised Learning Method

2020 ◽  
Vol 30 (07) ◽  
pp. 961-976
Author(s):  
Zeng Shu Shi ◽  
Yiman Du ◽  
Tao Du ◽  
Guochao Shan
Keyword(s):  
Supervised Learning ◽  
Learning Algorithm ◽  
Dynamic Properties ◽  
Support Vector ◽  
Electrical Characteristics ◽  
Railway Transportation ◽  
Machine Model ◽  
Diagnosis Method ◽  
The Cost ◽  
State Diagnosis

In China, the turnout abnormality very easily causes traffic accident or affects the efficiency due to the operating environment of railway transportation. The existing monitoring means are relatively backward, and mature automatic diagnosis method is lacking. In this study, a method based on semi-supervised learning algorithm for abnormal state diagnosis of turnout action curve is proposed, which is used to analyze and extract the electrical characteristics of the turnout by using the turnout action curve and the static and dynamic properties collected by the railway centralized monitoring system. The support vector machine model is used to construct the initial classifier with a small number of labeled samples, and the labeled samples are expanded from a large number of unlabeled samples. The switch curve is analyzed and diagnosed by using unlabeled data with a small amount of labeled data. The experimental results show that the method can automatically diagnose turnout electrical characteristics with high accuracy. While the cost of this method is relatively low compared with supervised learning, it can achieve higher accuracy and improve the practicability of fault diagnosis of turnout.

Classification and Recognition of Electronic Components Based on Improved Cooperative Semi-supervised Learning Algorithm

2020 ◽  
Vol 14 ◽  
Author(s):  
Dan Luo
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Supervised Learning ◽  
Image Recognition ◽  
Production Efficiency ◽  
Learning Algorithm ◽  
Electronic Components ◽  
Electron Device ◽  
Actual Recognition ◽  
The Subject

Background: As known that the semi-supervised algorithm is a classical algorithm in semi-supervised learning algorithm. Methods: In the paper, it proposed improved cooperative semi-supervised learning algorithm, and the algorithm process is presented in detailed, and it is adopted to predict unlabeled electronic components image. Results: In the experiments of classification and recognition of electronic components, it show that through the method the accuracy the proposed algorithm in electron device image recognition can be significantly improved, the improved algorithm can be used in the actual recognition process . Conclusion: With the continuous development of science and technology, machine vision and deep learning will play a more important role in people's life in the future. The subject research based on the identification of the number of components is bound to develop towards the direction of high precision and multi-dimension, which will greatly improve the production efficiency of electronic components industry.

scholarly journals Analysis of Operational Efficiencies and Costs for Extracting Thinned Woods in Small-Scale Forestry, Nasunogahara Area, Tochigi Prefecture, Japan

2020 ◽  
Vol 3 (1) ◽  
pp. 61
Author(s):  
Kazuhiro Aruga
Keyword(s):  
Transportation Costs ◽  
The Other ◽  
Small Scale ◽  
Light Truck ◽  
Operation Costs ◽  
Other Hand ◽  
Light Trucks ◽  
The Cost ◽  
Manual Extraction ◽  
Truck Transportation

In this study, two operational methodologies to extract thinned woods were investigated in the Nasunogahara area, Tochigi Prefecture, Japan. Methodology one included manual extraction and light truck transportation. Methodology two included mini-forwarder forwarding and four-ton truck transportation. Furthermore, a newly introduced chipper was investigated. As a result, costs of manual extractions within 10 m and 20 m were JPY942/m3 and JPY1040/m3, respectively. On the other hand, the forwarding cost of the mini-forwarder was JPY499/m3, which was significantly lower than the cost of manual extractions. Transportation costs with light trucks and four-ton trucks were JPY7224/m3 and JPY1298/m3, respectively, with 28 km transportation distances. Chipping operation costs were JPY1036/m3 and JPY1160/m3 with three and two persons, respectively. Finally, the total costs of methodologies one and two from extraction within 20 m to chipping were estimated as JPY9300/m3 and JPY2833/m3, respectively, with 28 km transportation distances and three-person chipping operations (EUR1 = JPY126, as of 12 August 2020).

scholarly journals Comparison of LPWAN Technologies: Cost Structure and Scalability

2021 ◽  
Author(s):  
Mohammad Istiak Hossain ◽  
Jan I. Markendahl
Keyword(s):  
Communication Technology ◽  
Service Providers ◽  
Communication Technologies ◽  
Cost Structure ◽  
Small Scale ◽  
Area Network ◽  
Wide Area Network ◽  
Generic Framework ◽  
Cost Efficient ◽  
The Cost

AbstractSmall-scale commercial rollouts of Cellular-IoT (C-IoT) networks have started globally since last year. However, among the plethora of low power wide area network (LPWAN) technologies, the cost-effectiveness of C-IoT is not certain for IoT service providers, small and greenfield operators. Today, there is no known public framework for the feasibility analysis of IoT communication technologies. Hence, this paper first presents a generic framework to assess the cost structure of cellular and non-cellular LPWAN technologies. Then, we applied the framework in eight deployment scenarios to analyze the prospect of LPWAN technologies like Sigfox, LoRaWAN, NB-IoT, LTE-M, and EC-GSM. We consider the inter-technology interference impact on LoRaWAN and Sigfox scalability. Our results validate that a large rollout with a single technology is not cost-efficient. Also, our analysis suggests the rollout possibility of an IoT communication Technology may not be linear to cost-efficiency.

scholarly journals Evaluating the Economic Viability of Agricultural Pellets to Supplement the Current Global Wood Pellets Supply for Bioenergy Production

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2263
Author(s):  
Mahmood Ebadian ◽  
Shahab Sokhansanj ◽  
David Lee ◽  
Alyssa Klein ◽  
Lawrence Townley-Smith
Keyword(s):  
Supply Chain ◽  
Western Europe ◽  
Small Scale ◽  
Economic Activities ◽  
Wood Pellets ◽  
Logistics System ◽  
Global Marketplace ◽  
Biomass Logistics ◽  
Production And Distribution ◽  
The Cost

In this study, an inter-continental agricultural pellet supply chain is modeled, and the production cost and price of agricultural pellets are estimated and compared against the recent cost and price of wood pellets in the global marketplace. The inter-continental supply chain is verified and validated using an integration of an interactive mapping application and a simulation platform. The integrated model is applied to a case study in which agricultural pellets are produced in six locations in Canada and shipped and discharged at the three major ports in Western Europe. The cost of agricultural pellets in the six locations is estimated to be in the range of EUR 92–95/tonne (CAD 138–142/tonne), which is comparable with the recent cost of wood pellets produced in small-scale pellet plants (EUR 99–109/tonne). The average agricultural pellet price shipped from the six plants to the three ports in Western Europe is estimated to be in a range of EUR 183–204 (CAD 274–305/tonne), 29–42% more expensive that the average recent price of wood pellets (EUR 143/tonne) at the same ports. There are several potential areas in the agricultural pellet supply chains that can reduce the pellet production and distribution costs in the mid and long terms, making them affordable supplement to the existing wood pellet markets. Potential economic activities generated by the production of pellets in farm communities can be significant. The generated annual revenue in the biomass logistics system in all six locations is estimated to be about CAD 21.80 million. In addition, the logistics equipment fleet needs 176 local operators with a potential annual income of CAD 2.18 million.

scholarly journals A Survey on Contrastive Self-Supervised Learning

Technologies ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 2
Author(s):  
Ashish Jaiswal ◽  
Ashwin Ramesh Babu ◽  
Mohammad Zaki Zadeh ◽  
Debapriya Banerjee ◽  
Fillia Makedon
Keyword(s):  
Computer Vision ◽  
Supervised Learning ◽  
Language Processing ◽  
Large Scale ◽  
Performance Comparison ◽  
Extensive Review ◽  
Future Directions ◽  
Dominant Component ◽  
Supervised Methods ◽  
The Cost

Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as supervision and use the learned representations for several downstream tasks. Specifically, contrastive learning has recently become a dominant component in self-supervised learning for computer vision, natural language processing (NLP), and other domains. It aims at embedding augmented versions of the same sample close to each other while trying to push away embeddings from different samples. This paper provides an extensive review of self-supervised methods that follow the contrastive approach. The work explains commonly used pretext tasks in a contrastive learning setup, followed by different architectures that have been proposed so far. Next, we present a performance comparison of different methods for multiple downstream tasks such as image classification, object detection, and action recognition. Finally, we conclude with the limitations of the current methods and the need for further techniques and future directions to make meaningful progress.

scholarly journals Discovering latent node Information by graph attention network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Weiwei Gu ◽  
Fei Gao ◽  
Xiaodan Lou ◽  
Jiang Zhang
Keyword(s):  
Supervised Learning ◽  
Link Prediction ◽  
Learning Algorithm ◽  
Network Visualization ◽  
Graph Structure ◽  
High Quality ◽  
Node Centrality ◽  
Influential Nodes ◽  
Node Classification ◽  
Highly Cited

AbstractIn this paper, we propose graph attention based network representation (GANR) which utilizes the graph attention architecture and takes graph structure as the supervised learning information. Compared with node classification based representations, GANR can be used to learn representation for any given graph. GANR is not only capable of learning high quality node representations that achieve a competitive performance on link prediction, network visualization and node classification but it can also extract meaningful attention weights that can be applied in node centrality measuring task. GANR can identify the leading venture capital investors, discover highly cited papers and find the most influential nodes in Susceptible Infected Recovered Model. We conclude that link structures in graphs are not limited on predicting linkage itself, it is capable of revealing latent node information in an unsupervised way once a appropriate learning algorithm, like GANR, is provided.

scholarly journals A Threat of Farmers’ Suicide and the Opportunity in Organic Farming for Sustainable Agricultural Development in India

2019 ◽  
Vol 11 (8) ◽  
pp. 2400 ◽  
Author(s):  
Karthikeyan Mariappan ◽  
Deyi Zhou
Keyword(s):  
Organic Farming ◽  
Tamil Nadu ◽  
Agricultural Development ◽  
Small Scale ◽  
Conventional Farming ◽  
Wide Range ◽  
Significant Difference ◽  
Small Scale Farmers ◽  
The Cost ◽  
Input Cost

Agriculture is the main sources of income for humans. Likewise, agriculture is the backbone of the Indian economy. In India, Tamil Nadu regional state has a wide range of possibilities to produce all varieties of organic products due to its diverse agro-climatic condition. This research aimed to identify the economics and efficiency of organic farming, and the possibilities to reduce farmers’ suicides in the Tamil Nadu region through the organic agriculture concept. The emphasis was on farmers, producers, researchers, and marketers entering the sustainable economy through organic farming by reducing input cost and high profit in cultivation. A survey was conducted to gather data. One way analysis of variance (ANOVA) has been used to test the hypothesis regards the cost and profit of rice production. The results showed that there was a significant difference in profitability between organic and conventional farming methods. It is very transparent that organic farming is the leading concept of sustainable agricultural development with better organic manures that can improve soil fertility, better yield, less input cost and better return than conventional farming. The study suggests that by reducing the cost of cultivation and get a marginal return through organic farming method to poor and small scale farmers will reduce socio-economic problems such as farmers’ suicides in the future of Indian agriculture.

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