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Optimal Policy for Deployment of Machine Learning Models on Energy-Bounded Systems

Seyed Iman Mirzadeh, Hassan Ghasemzadeh
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
Compared to a pruned convolutional neural network for human activity recognition, while consuming 1.7% less energy, our proposed policy achieves 1.3% higher accuracy.  ...  With the recent advances in both machine learning and embedded systems research, the demand to deploy computational models for real-time execution on edge devices has increased substantially.  ...  In this study, we propose to construct the causal graph G with the following steps: Firstly, the EMR document undergoes the named entity recognition (NER) [Dai et al., 2019] to extract the medical entities  ... 
doi:10.24963/ijcai.2020/469 dblp:conf/ijcai/YuanCLH20 fatcat:swputkn6hnagtgadttmzvphgza

Knowledge-based Biomedical Data Science 2019 [article]

Tiffany J. Callahan, Ignacio J. Tripodi Computational Bioscience Program, Department of Pharmacology, University of Colorado Denver Anschutz Medical Campus
2019 arXiv   pre-print
Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese  ...  knowledge graphs.  ...  Neural networks for link Jiang J, Wang H, Xie J, Guo X, Guan Y, Yu Q. 2018. Medical Knowledge Embedding Based on Recursive Neural Network for Multi-Disease Diagnosis.  ... 
arXiv:1910.06710v1 fatcat:kvz5k643zvhpdiq67blc2v33wi

Fine-Grained Drug Interaction Extraction Based on Entity Pair Calibration and Pre-Training Model for Chinese Drug Instructions

Xiaoliang Zhang, Feng Gao, Lunsheng Zhou, Shenqi Jing, Zhongmin Wang, Yongqing Wang, Shumei Miao, Xin Zhang, Jianjun Guo, Tao Shan, Yun Liu
2022 International Journal on Semantic Web and Information Systems (IJSWIS)  
The framework proposes deep learning models with fine-tuned pre-training models for entity recognition and relation extraction, in addition, it incorporates an novel entity pair calibration process to  ...  The framework experiments on more than 60k Chinese drug description sentences from 4000 drug instructions.  ...  After the entity name recognition, it gathers the sentences with multiple relevant entities and applies the entity pair calibration (EPC) process.  ... 
doi:10.4018/ijswis.307908 fatcat:ikz6l5cqivd6bfd3f5dzira3ye

Domain-specific Knowledge Graphs: A survey [article]

Bilal Abu-Salih
2021 arXiv   pre-print
Knowledge Graphs (KGs) have made a qualitative leap and effected a real revolution in knowledge representation.  ...  This is leveraged by the underlying structure of the KG which underpins a better comprehension, reasoning and interpretation of knowledge for both human and machine.  ...  The construction of the KG involved incorporating Named Entities Recognition (NER) and Neural Relation Extraction (NRE) for entities extraction and Convolutional Neural Network (CNN) for relation inference  ... 
arXiv:2011.00235v3 fatcat:oc2loewqdjfgvlapy4kmult5li

Survey of NLP in Pharmacology: Methodology, Tasks, Resources, Knowledge, and Tools [article]

Dimitar Trajanov, Vangel Trajkovski, Makedonka Dimitrieva, Jovana Dobreva, Milos Jovanovik, Matej Klemen, Aleš Žagar, Marko Robnik-Šikonja
2022 arXiv   pre-print
This area has rapidly developed in the last few years and now employs modern variants of deep neural networks to extract relevant patterns from large text corpora.  ...  As our work shows, NLP is a highly relevant information extraction and processing approach for pharmacology.  ...  Acknowledgments This work is also based on COST Action CA18209 -NexusLinguarum "European network for Web-centred linguistic data science", supported by COST (European Cooperation in Science and Technology  ... 
arXiv:2208.10228v1 fatcat:76kwzhx53fbfpowwjie5neeicu

Multichannel CNN Model for Biomedical Entity Reorganization

Ajay Kumar Singh, Ihtiram Raza Khan, Shakir Khan, Kumud Pant, Sandip Debnath, Shahajan Miah, B. D. Parameshachari
2022 BioMed Research International  
The extraction of biological entity relationships is the foundation for achieving intelligent medical care, which may increase the effectiveness of intelligent medical question answering and enhance the  ...  development of precision healthcare.  ...  In addition, prior knowledge has a better role in promoting named entity recognition tasks, such as knowledge graphs.  ... 
doi:10.1155/2022/5765629 pmid:35345527 pmcid:PMC8957457 fatcat:w7gziimnbzfc5asxcoc7rjjud4

A Concise Survey on Datasets, Tools and Methods for Biomedical Text

R. Johnsi, G. Bharadwaja Kumar, Tulasi Prasad Sariki
2022 International Journal of Applied Engineering Research  
Since the text data is unstructured, it becomes difficult for the experts and researchers to analyze the above information manually or to extract information automatically.  ...  Biomedical Named Entity Recognition task Biomedical Named Entity Recognition (Bio-NER) is used to automatically recognize Biomedical entities (e.g., chemicals, diseases and Proteins) in given texts.  ...  Recognizing Named entities that appear with multiple phrases is a difficult task. 3.  ... 
doi:10.37622/ijaer/17.3.2022.200-217 fatcat:2cw7f572rjfqvbnbrpq7gc75ue

SUDIR: An Approach of Sensing Urban Text Data from Internet Resources based on Deep Learning

Chaoran Zhou, Jianping Zhao, Chenghao Ren
2020 IEEE Access  
NAMED ENTITY RECOGNITION FOR URBAN DATA Extracting urban text data from Internet resources is a sequential labeling task, i.e., the application of Name Entity Recognition (NER) in urban data processing  ...  The deep neural network structure of BERT-WWM+BLSTM-CRF urban data recognition model proposed for SUDIR's urban data recognition module includes three layers, i.e., BERT-WWM word embedding layer, BLSTM  ...  For more information, see  ... 
doi:10.1109/access.2020.3040408 fatcat:5cgd4soqprhaxfd64imk7hf22y

Decentralized Construction of Knowledge Graphs for Deep Recommender Systems Based on Blockchain-Powered Smart Contracts

Shuai Wang, Chenchen Huang, Juanjuan Li, Yong Yuan, Fei-Yue Wang
2019 IEEE Access  
This paper is aimed at providing a novel decentralized approach for constructing knowledge graph and serving as reference and guidance for future research and practical applications of knowledge graph.  ...  On this basis, the decentralized knowledge graph is used for a deep recommender system, and case studies validate the effectiveness of the system.  ...  Named entity recognition (NER) [8] and relation extraction [9] are often used in this step. 2) Knowledge fusion.  ... 
doi:10.1109/access.2019.2942338 fatcat:xrteb25knjan7pky7r2p4umdg4

A Survey on Knowledge Graphs: Representation, Acquisition and Applications [article]

Shaoxiong Ji and Shirui Pan and Erik Cambria and Pekka Marttinen and Philip S. Yu
2021 IEEE Transactions on Neural Networks and Learning Systems   accepted
For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed.  ...  Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence.  ...  Variational Reasoning Network (VRN) [200] conducts multi-hop logic reasoning with reasoning-graph embedding, while handles the uncertainty in topic entity recognition.  ... 
doi:10.1109/tnnls.2021.3070843 pmid:33900922 arXiv:2002.00388v4 fatcat:4l2yxnf3wbg4zpzdumduvyr4he

Construction of Power Fault Knowledge Graph Based on Deep Learning

Peishun Liu, Bing Tian, Xiaobao Liu, Shijing Gu, Li Yan, Leon Bullock, Chao Ma, Yin Liu, Wenbin Zhang
2022 Applied Sciences  
Aiming to solve the problems of large calculation cost and propagation error which occur in the traditional relationship extraction model, an improved Bidirectional Gated Recurrent Unit neural network  ...  recognition of fault sentences.  ...  Acknowledgments: We are grateful to the anonymous reviewers for comments on the original manuscript. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app12146993 fatcat:fmhgglorl5hltah65k77zctdyu

Fine-Grained Mechanical Chinese Named Entity Recognition Based on ALBERT-AttBiLSTM-CRF and Transfer Learning

Liguo Yao, Haisong Huang, Kuan-Wei Wang, Shih-Huan Chen, Qiaoqiao Xiong
2020 Symmetry  
active learning (MTAL) to research fine-grained named entity recognition of a few labeled Chinese textual data types.  ...  This paper proposes a novel Chinese fine-grained NER (named entity recognition) method based on symmetry lightweight deep multinetwork collaboration (ALBERT-AttBiLSTM-CRF) and model transfer considering  ...  Named Entity Recognition for Domain Fields Zhou et al. [24] proposed an in-depth neural network for bug-specific entity recognition (DBNER) using LSTM with CRF.  ... 
doi:10.3390/sym12121986 fatcat:sz2yjho5ljcydfgzfh7whh5vlm

Structured Knowledge Discovery from Massive Text Corpus [article]

Chenwei Zhang
2019 arXiv   pre-print
On the other hand, the existing structured information, which embodies our knowledge such as entity or concept relations, often suffers from incompleteness or quality-related issues.  ...  In this dissertation, I will introduce principles, models, and algorithms for effective structured knowledge discovery from the massive text corpus.  ...  A neural network model named coCTI-MTL based on multi-task learning is introduced to extract concept mentions as well as semantic transitions collectively as a sub-graph of the Intent Graph to represent  ... 
arXiv:1908.01837v1 fatcat:j46srlxblfd35cd4z6jkl43iiu

IEEE Access Special Section Editorial: AI-Driven Big Data Processing: Theory, Methodology, and Applications

Zhanyu Ma, Sunwoo Kim, Pascual Martinez-Gomez, Jalil Taghia, Yi-Zhe Song, Huiji Gao
2020 IEEE Access  
., ''Multi-gram CNN-based self-attention model for relation classification,'' proposes a novel multi-gram convolution neural network-based selfattention model with a recurrent neural network framework.  ...  Most deep learning-based algorithms for reID extract global embedding as the representation of the pedestrian from the convolutional neural network.  ...  The authors use the single-shot refinement neural network (RefineDet) as a base network, which employs top-down architecture to offer contextual information, achieving accurate detection.  ... 
doi:10.1109/access.2020.3035461 fatcat:rt7ejtponrfexigie4cfpt7gd4

Natural Language Processing for Information Extraction [article]

Sonit Singh
2018 arXiv   pre-print
Various sub-tasks of IE such as Named Entity Recognition, Coreference Resolution, Named Entity Linking, Relation Extraction, Knowledge Base reasoning forms the building blocks of various high end Natural  ...  This explosion of information and need for more sophisticated and efficient information handling tools gives rise to Information Extraction(IE) and Information Retrieval(IR) technology.  ...  ., 2016 proposed attention based Convolutional Neural Network (CNN) architecture which uses word embedding, part-of-speech tag embedding and position embedding information.  ... 
arXiv:1807.02383v1 fatcat:3bdyidbjp5hn7c2w4iqve4ajvi
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