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Named Entity Recognition and Linking Augmented with Large-Scale Structured Data [article]

Paweł Rychlikowski, Bartłomiej Najdecki, Adrian Łańcucki, Adam Kaczmarek
2021 arXiv   pre-print
With the aid of those resources, our system could recognize, normalize and link entities, while being trained with only small amounts of labeled data.  ...  Our solution takes advantage of large collections of both unstructured and structured documents. The former serve as data for unsupervised training of language models and embeddings of lexical units.  ...  We also would like to thank Adam Wawrzyński and Wojciech Janowski from VoiceLab AI for their support during conducting experiments and model training.  ... 
arXiv:2104.13456v1 fatcat:z5y3anpmzzbtpaissmccpra3re

Low-Resource Named Entity Recognition via the Pre-Training Model

Siqi Chen, Yijie Pei, Zunwang Ke, Wushour Silamu
2021 Symmetry  
We close with an overview of the available resources for named entity recognition and some of the open research questions.  ...  features to name entity recognition tasks.  ...  Acknowledgments: The authors are very thankful to the editor and referees for their valuable comments and suggestions for improving the paper.  ... 
doi:10.3390/sym13050786 fatcat:w5wswjvdavcqjbde7at2vee4oi

Retrieval, Crawling and Fusion of Entity-centric Data on the Web [chapter]

Stefan Dietze
2017 Lecture Notes in Computer Science  
graphs and linked data.  ...  On the one hand, recommendation, linking, profiling and retrieval can provide efficient means to enable discovery and search of entity-centric data, specifically when dealing with traditional knowledge  ...  While all discussed works are joint research with numerous colleagues, friends and collaborators from a number of research institutions, the author would like to thank all involved researchers for the  ... 
doi:10.1007/978-3-319-53640-8_1 fatcat:bilhfrwhgvgwnfm3wub4g55lr4

NextGen AML: Distributed Deep Learning based Language Technologies to Augment Anti Money Laundering Investigation

Jingguang Han, Utsab Barman, Jeremiah Hayes, Jinhua Du, Edward Burgin, Dadong Wan
2018 Proceedings of ACL 2018, System Demonstrations  
The proposed distributed framework performs news and tweet sentiment analysis, entity recognition, relation extraction, entity linking and link analysis on different data sources (e.g. news articles and  ...  and scalable manner to augment AML monitoring and investigation.  ...  Acknowledgements Many thanks to the reviewers for their insightful comments and suggestions.  ... 
doi:10.18653/v1/p18-4007 dblp:conf/acl/HanBHDBW18 fatcat:4xed34wvtfhf3bxi2w6rmslqxi

Improving the recall of biomedical named entity recognition with label re-correction and knowledge distillation

Huiwei Zhou, Zhe Liu, Chengkun Lang, Yibin Xu, Yingyu Lin, Junjie Hou
2021 BMC Bioinformatics  
Firstly, for coverage, we annotate chemical and disease entities in a large-scale unlabeled dataset by PubTator to generate a weakly labeled dataset.  ...  Methods To remedy the above issue, we propose a novel Biomedical Named Entity Recognition (BioNER) framework with label re-correction and knowledge distillation strategies, which could not only create  ...  Acknowledgements We would like to thank the editors and all anonymous reviewers for valuable suggestions and constructive comments Authors' contributions HZ and ZL designed the study.  ... 
doi:10.1186/s12859-021-04200-w pmid:34078270 fatcat:2zbg3vqqvjcmjoqe24nrhzhdrq

Data-Augmented Hybrid Named Entity Recognition for Disaster Management by Transfer Learning

Hung-Kai Kung, Chun-Mo Hsieh, Cheng-Yu Ho, Yun-Cheng Tsai, Hao-Yung Chan, Meng-Han Tsai
2020 Applied Sciences  
This research aims to build a Mandarin named entity recognition (NER) module using transfer learning to facilitate damage information gathering and analysis in disaster management.  ...  long short-term memory–conditional random field framework to implement NER; and (3) the augmented model built by integrating the first two modules via cross-domain transfer with disparate label sets.  ...  Acknowledgments: The authors are appreciated and grateful to the Human-Computer Interaction Lab, Department of Civil and Construction Engineering, NTUST, for the enlightenment and support of the research  ... 
doi:10.3390/app10124234 fatcat:mdbs3bjv45hffbg6pzvlf55bp4

Data Augmentation for Personal Knowledge Base Population [article]

Lingraj S Vannur, Balaji Ganesan, Lokesh Nagalapatti, Hima Patel, MN Thippeswamy
2020 arXiv   pre-print
This problem is more acute in personal knowledge bases, which present additional challenges with regard to data protection, fairness and privacy.  ...  In this work, we present a system that uses rule based annotators and a graph neural network for missing link prediction, to populate a more complete, fair and diverse knowledge base from the TACRED dataset  ...  This method requires creation of dictionaries each named after the entity type, and populated with entity mentions.  ... 
arXiv:2002.10943v2 fatcat:nub7jctfkvbz5atv3o2h6xj2he

An Imperative Focus on Semantic Web Principles, Logics and its Application

Senthil Kumar N, Dinakaran M
2015 The International Journal of Ambient Systems and Applications  
In this research article, we have given the clear implication of semantic web research roots and its ontological background process which may help to augment the sheer understanding of named entities in  ...  data models such as RDF/RDFS.  ...  The bigger task of the Named Entity Recognition [11] is that extracts the atomic entities from the chosen text and dynamically links them to the respective linked data entities.  ... 
doi:10.5121/ijasa.2014.3101 fatcat:7mr7rsq23rfrnlluzhy4xm4a3e

The SUMMA Platform Prototype

Renars Liepins, Ulrich Germann, Guntis Barzdins, Alexandra Birch, Steve Renals, Susanne Weber, Peggy van der Kreeft, Herve Bourlard, João Prieto, Ondrej Klejch, Peter Bell, Alexandros Lazaridis (+34 others)
2017 Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics  
of named entities, semantic parsing to detect relationships between entities, and automatic construction / augmentation of factual knowledge bases.  ...  Implemented on the Docker platform, it can easily be deployed, customised, and scaled to large volumes of incoming media streams.  ...  (plaintext), and automatic translation with recognized named entities marked up.  ... 
doi:10.18653/v1/e17-3029 dblp:conf/eacl/LiepinsGBBRWKBP17 fatcat:4hr72aky2vgmtdks45iuarz7lq

CREER: A Large-Scale Corpus for Relation Extraction and Entity Recognition [article]

Yu-Siou Tang, Chung-Hsien Wu
2022 arXiv   pre-print
We describe the design and use of the CREER dataset, a large corpus annotated with rich English grammar and semantic attributes.  ...  This dataset follows widely used linguistic and semantic annotations so that it can be used for not only most natural language processing tasks but also scaling the dataset.  ...  and the Ministry of Science and Technology of Taiwan for financially supporting this research under contract no. 108-2221-E-006 -103 -MY3.  ... 
arXiv:2204.12710v1 fatcat:72zqdpjtdvdt7bqouwvtcna6mq

Text/Conference Paper

Florian Niebling, Michael Haas, Andre´ Blessing
2019 Jahrestagung der Gesellschaft für Informatik  
We highlight the potentials of incorporating machine learning with different models for named-entity recognition in image captions, to facilitate classification of images according to standard IPTC media  ...  To provide easy access, image databases need to be structured and photographic documents stored within need to be categorized.  ...  In this work, we are trying to augment and support the tagging of images with image classiĄcation techniques and algorithms from computational linguistics, i.e. named entity recognition, to provide knowledge  ... 
doi:10.18420/inf2019_ws17 dblp:conf/gi/NieblingHB19 fatcat:jzltd4lacrgoxne6pf6uivb5pa

Low-resource Learning with Knowledge Graphs: A Comprehensive Survey [article]

Jiaoyan Chen and Yuxia Geng and Zhuo Chen and Jeff Z. Pan and Yuan He and Wen Zhang and Ian Horrocks and Huajun Chen
2021 arXiv   pre-print
dividing them into different paradigms such as the mapping-based, the data augmentation, the propagation-based and the optimization-based.  ...  ., dynamic contexts with emerging prediction targets and costly sample annotation.  ...  Large-scale few-shot learning: Knowledge transfer with class hierarchy.  ... 
arXiv:2112.10006v3 fatcat:wkz6gjx4r5gvlhh673p3rqsmgi

Entity Relation Extraction as Dependency Parsing in Visually Rich Documents [article]

Yue Zhang, Bo Zhang, Rui Wang, Junjie Cao, Chen Li, Zuyi Bao
2021 arXiv   pre-print
We have compared different representations of the semantic entity, different VRD encoders, and different relation decoders.  ...  ., semantic entity), while the relations in-between are largely unexplored. In this paper, we adapt the popular dependency parsing model, the biaffine parser, to this entity relation extraction task.  ...  further improve the performance to 65.96%, i.e., multi-task learning with entity labeling and data augmentation.  ... 
arXiv:2110.09915v1 fatcat:csidkisb4bf6jfwo72maaopm3a

Relational Representation Learning in Visually-Rich Documents [article]

Xin Li, Yan Zheng, Yiqing Hu, Haoyu Cao, Yunfei Wu, Deqiang Jiang, Yinsong Liu, Bo Ren
2022 arXiv   pre-print
DocReL achieves better performance on a wide variety of VRD relational understanding tasks, including table structure recognition, key information extraction and reading order detection.  ...  To deal with the unpredictable definition of relations, we propose a novel contrastive learning task named Relational Consistency Modeling (RCM), which harnesses the fact that existing relations should  ...  SciTSR [6] is a large-scale table structure recognition dataset, which contains 15,000 tables in PDF format and their corresponding structure labels obtained from LaTeX source files.  ... 
arXiv:2205.02411v1 fatcat:xqacpmjpxfhrvmzgrimhpcxuna

Using Wikipedia for Cross-Language Named Entity Recognition [chapter]

Eraldo R. Fernandes, Ulf Brefeld, Roi Blanco, Jordi Atserias
2016 Lecture Notes in Computer Science  
Named entity recognition and classification (NERC) is fundamental for natural language processing tasks such as information extraction, question answering, and topic detection.  ...  To learn from such partially annotated data, we devise two simple extensions of hidden Markov models and structural perceptrons.  ...  [31] aim to improve named entity recognition and classification for English Wikipedia entries using key-value pairs of the semi-structured info boxes.  ... 
doi:10.1007/978-3-319-29009-6_1 fatcat:fztpp4x52fchpmi6snmothrcqy
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