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A Framework Using Active Learning to Rapidly Perform Named Entity Extraction and Relation Recognition for Science and Technology Knowledge Graph

Ying Wang, Jing Dong, Peng Ren, Ye Wang, Jingjing Cao
2020 Open Journal of Social Sciences  
This framework combines the human and machine learning approach together, which is active learning, to help user extract entity from those unstructured data with less time cost.  ...  In this paper, we design and proposed a framework using active learning; this framework can be used to extract entity and relation from unstructured science and technology research data.  ...  Human-in-the-loop has actually been applied to many aspects of artificial intelligence like named entity recognition (Coelho da Silva & Magalhães et al., 2019) and rules learning (Yang, Kandogan, Li,  ... 
doi:10.4236/jss.2020.89025 fatcat:ng3qlpefr5hejno5clrzrksire

An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records

Luqi Li, Jie Zhao, Li Hou, Yunkai Zhai, Jinming Shi, Fangfang Cui
2019 BMC Medical Informatics and Decision Making  
clinical named entity recognition of Chinese EMRs.  ...  It is of great importance to eliminate semantic interference and improve the ability of autonomous learning of internal features of the model under the small training corpus.  ...  Availability of data and materials The datasets used in this study are adopted from the Chinese EMR named entity recognition task in China Conference on Knowledge Graph and Semantic Computing in 2018 (  ... 
doi:10.1186/s12911-019-0933-6 pmid:31801540 pmcid:PMC6894110 fatcat:c6wgahdbdzccfpcmon3gus4bxi

Named entity recognition on bio-medical literature documents using hybrid based approach

R. Ramachandran, K. Arutchelvan
2021 Journal of Ambient Intelligence and Humanized Computing  
The annotated entities are trained by the blank Spacy machine learning model. The trained model provide a decent accuracy when compared with the existing model.  ...  The hybrid model is validated with the dictionary and human (optional)to calculate the confusion matrix. It is able to identify more entities than the prevailing model.  ...  This is one of the most powerful NLP resource to identify the named entities. BERT is developed using the deep learning concepts and trained with 2.5 billion words .  ... 
doi:10.1007/s12652-021-03078-z pmid:33723489 pmcid:PMC7947151 fatcat:yzjuwt52pbdx7m2aapy6wasfhq

The Application of Deep Learning Model in Recruitment Decision

Yang Wang, Zhengjie Zhu, Xin Ning
2022 Wireless Communications and Mobile Computing  
Starting with the existing deep learning, from four models, that is, based on the traditional machine learning model, conditional random field (CRF), deep learning models Bi-LSTM-CRF, BERT, and BERT-Bi-LSTM-CRF  ...  identify and automatically extract recruitment entities and study recruitment accordingly; BERT-Bi-LSTM-CRF-BERT and BERT-BiLSTM-CRF are the models with the worst recognition effect.  ...  to leave the problem research-take chain catering enterprises in Anhui Province as an example (tzpyxj130  ... 
doi:10.1155/2022/9645830 fatcat:yjgn5kp2xnccnb6pargi2e3c4i

OPAD: An Optimized Policy-based Active Learning Framework for Document Content Analysis [article]

Sumit Shekhar, Bhanu Prakash Reddy Guda, Ashutosh Chaubey, Ishan Jindal, Avneet Jain
2021 arXiv   pre-print
We show superior performance of the proposed OPAD framework for active learning for various tasks related to document understanding like layout parsing, object detection and named entity recognition.  ...  There have been recent spurt of interest in understanding document content with novel deep learning architectures.  ...  and named entity recognition.  ... 
arXiv:2110.02069v2 fatcat:ynrqxqya3vflrnnn2cz5dtgzqy

Combining Data-driven Supervision with Human-in-the-loop Feedback for Entity Resolution [article]

Wenpeng Yin, Shelby Heinecke, Jia Li, Nitish Shirish Keskar, Michael Jones, Shouzhong Shi, Stanislav Georgiev, Kurt Milich, Joseph Esposito, Caiming Xiong
2021 arXiv   pre-print
In this case study, we discuss our human-in-the-loop enabled, data-centric solution to closing the training-production performance divergence.  ...  We conclude with takeaways that apply to data-centric learning at large.  ...  ., nickname recognition. V2: deep learning with data augmentation System.  ... 
arXiv:2111.10497v1 fatcat:m7wkmilsivfmre5d2tf6bttt3q

Human-in-the-Loop Conversation Agent for Customer Service [chapter]

Pēteris Paikens, Artūrs Znotiņš, Guntis Bārzdiņš
2020 Lecture Notes in Computer Science  
This paper describes a prototype system for partial automation of customer service operations of a mobile telecommunications operator with a human-in-the loop conversational agent.  ...  messages in Latvian.  ...  This research is funded by the Latvian Council of Science, project "Latvian Language Understanding and Generation in Human-Computer Interaction", project No. LZP-2018/2-0216.  ... 
doi:10.1007/978-3-030-51310-8_25 fatcat:3tqni6drqjb2lf66376kvxwx3i

Regular Expression Guided Entity Mention Mining from Noisy Web Data

Shanshan Zhang, Lihong He, Slobodan Vucetic, Eduard Dragut
2018 Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing  
Rather than abandoning REs as a go-to approach for entity detection, this paper explores ways to combine the expressive power of REs, ability of deep learning to learn from large data, and human-in-the  ...  loop approach into a new integrated framework for entity identification from web data.  ...  Acknowledgments This work was supported in part by the following grants: U.S. NSF BigData 1546480 and U.S. NIH R21CA202130.  ... 
doi:10.18653/v1/d18-1224 dblp:conf/emnlp/ZhangHVD18 fatcat:f3ux3kzm25e5tprxnbhmdvvhqe

AutoTriggER: Named Entity Recognition with Auxiliary Trigger Extraction [article]

Dong-Ho Lee, Ravi Kiran Selvam, Sheikh Muhammad Sarwar, Bill Yuchen Lin, Mahak Agarwal, Fred Morstatter, Jay Pujara, Elizabeth Boschee, James Allan, Xiang Ren
2021 arXiv   pre-print
Deep neural models for low-resource named entity recognition (NER) have shown impressive results by leveraging distant super-vision or other meta-level information (e.g. explanation).  ...  In this paper, we present a novel two-stage framework (AutoTriggER) to improve NER performance by automatically generating and leveraging "entity triggers" which are essentially human-readable clues in  ...  Human-in-the-loop Trigger Extraction Human-curated vs. Auto Triggers.  ... 
arXiv:2109.04726v2 fatcat:6fmnnlmrhjbgbfjilmihew4qpe

An adaptive annotation approach for biomedical entity and relation recognition

Seid Muhie Yimam, Chris Biemann, Ljiljana Majnaric, Šefket Šabanović, Andreas Holzinger
2016 Brain Informatics  
In this article, we demonstrate the impact of interactive machine learning: we develop biomedical entity recognition dataset using a human-into-the-loop approach.  ...  In contrary to classical machine learning, human-in-theloop approaches do not operate on predefined training or test sets, but assume that human input regarding system improvement is supplied iteratively  ...  Acknowledgments The development of WebAnno and the research on adaptive machine learning was supported by the German Federal Ministry of Education and Research (BMBF) as part of the CLARIN-D infrastructure  ... 
doi:10.1007/s40708-016-0036-4 pmid:27747591 pmcid:PMC4999566 fatcat:fl6greheunc5lga3kvof647li4

Unsupervised Approaches for Textual Semantic Annotation, A Survey

Xiaofeng Liao, Zhiming Zhao
2019 ACM Computing Surveys  
Link to publication Creative Commons License (see https://creativecommons.org/use-remix/cc-licenses): CC BY Citation for published version (APA):  ...  ACKNOWLEDGMENTS The authors thank the anonymous reviewers for their helpful comments, in addition to Cees de Laat, Paul Martin, Jayachander Surbiryala, and ZeShun Shi for useful discussions.  ...  A detailed examination regarding named entity recognition using deep learning models can be found in Yadav and Bethard (2018) . Relation Extraction.  ... 
doi:10.1145/3324473 fatcat:fg5ucwtloze6ljdlh4hqjkqxfe

Improving Publication Pipeline with Automated Biological Entity Detection and Validation Service

Weijia Xu, Amit Gupta, Pankaj Jaiswal, Crispin Taylor, Patti Lockhart, Jennifer Regala
2019 Data and Information Management  
The updates can then be used to improve the entity detection in subsequent processed articles in the future. We describe our framework and deployment in details.  ...  One common task is to identify useful information and named entity from text documents such as journal article submission.  ...  The application integrates multiple NLP methods for entity recognition and enables human curation to close the feedback loop.  ... 
doi:10.2478/dim-2019-0003 fatcat:w4swautdkbefti3gfy4scsle5u

A BERT-BiGRU-CRF Model for Entity Recognition of Chinese Electronic Medical Records

Qiuli Qin, Shuang Zhao, Chunmei Liu, Abd E.I.-Baset Hassanien
2021 Complexity  
Excellent research results have been achieved in the field of named entity recognition (NER), but there are several problems in the pre processing of Chinese named entities that have multiple meanings,  ...  Because of difficulty processing the electronic medical record data of patients with cerebrovascular disease, there is little mature recognition technology capable of identifying the named entity of cerebrovascular  ...  and treatment of major diseases in Beijing's medical reform.  ... 
doi:10.1155/2021/6631837 fatcat:hr3gflfatvdsrk2m254ip2b4hq

CASPR: A Commonsense Reasoning-based Conversational Socialbot [article]

Kinjal Basu, Huaduo Wang, Nancy Dominguez, Xiangci Li, Fang Li, Sarat Chandra Varanasi, Gopal Gupta
2021 arXiv   pre-print
We report on the design and development of the CASPR system, a socialbot designed to compete in the Amazon Alexa Socialbot Challenge 4.  ...  CASPR's distinguishing characteristic is that it will use automated commonsense reasoning to truly "understand" dialogs, allowing it to converse like a human.  ...  Acknowledgments The CASPR team would like to thank Amazon for giving it the opportunity to participate in the competition. The Amazon team's support and help were wonderful and indispensable.  ... 
arXiv:2110.05387v1 fatcat:yv3d3k7ysve5fpmlecp53ahcqu

Putting hands to rest: efficient deep CNN-RNN architecture for chemical named entity recognition with no hand-crafted rules

Ilia Korvigo, Maxim Holmatov, Anatolii Zaikovskii, Mikhail Skoblov
2018 Journal of Cheminformatics  
Here we explored this approach by experimenting with various deep learning architectures for targeted tokenisation and named entity recognition.  ...  Chemical named entity recognition (NER) is an active field of research in biomedical natural language processing.  ...  Acknowledgements This work was carried out in collaboration between the Laboratory of Functional Analysis of the Genome (Moscow State Institute of Physics and Technology, Moscow, Russia) and the Laboratory  ... 
doi:10.1186/s13321-018-0280-0 pmid:29796778 pmcid:PMC5966369 fatcat:47rjjknesragtjuwapu3jqiz6m
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