We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Progress in Deep Learning Mechanisms for Information Extraction from Social Networks: An Expository Overview

by Israel Fianyi, Gifty Andoh Appiah
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 24
Year of Publication: 2021
Authors: Israel Fianyi, Gifty Andoh Appiah
10.5120/ijca2021921155

Israel Fianyi, Gifty Andoh Appiah . Progress in Deep Learning Mechanisms for Information Extraction from Social Networks: An Expository Overview. International Journal of Computer Applications. 174, 24 ( Mar 2021), 50-63. DOI=10.5120/ijca2021921155

@article{ 10.5120/ijca2021921155,
author = { Israel Fianyi, Gifty Andoh Appiah },
title = { Progress in Deep Learning Mechanisms for Information Extraction from Social Networks: An Expository Overview },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2021 },
volume = { 174 },
number = { 24 },
month = { Mar },
year = { 2021 },
issn = { 0975-8887 },
pages = { 50-63 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume174/number24/31826-2021921155/ },
doi = { 10.5120/ijca2021921155 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:23:01.762418+05:30
%A Israel Fianyi
%A Gifty Andoh Appiah
%T Progress in Deep Learning Mechanisms for Information Extraction from Social Networks: An Expository Overview
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 24
%P 50-63
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Deep learning algorithms have shown to be robust in extracting high quality information from a wide range of online platforms. Incidentally, social networks and other related online platforms are known to hold a copious amount of unstructured user-generated content. To date, machine learning and deep learning approaches for mining textual data have received so much attention from researchers and industry players Deep learning is good at independently learning from complex feature representation and make intelligent decisions from data. However, with the influx if different deep learning methods for information extraction, understanding and finding the current challenges and recent advances in these algorithms is daunting. This paper investigates existing pieces of literature to appreciate the trajectory of deep learning for information extraction in Natural Language Understanding. The study further considers the state-of-the-art, open challenges, as well as the tools and methodologies involved in undertaking information extraction tasks from Unstructured data. The study considers relevant published articles from the year 2009-2020 that focused on deep learning approach for information extraction from text. The investigations of this paper provide extensive clarity to the research field of Natural Language Processing with deep learning. It identifies current research problems, recommends directions for future research. The paper is designed to help non-expert researchers comprehend the fundamentals of deep learning and Natural Language Processing methods for Information Extraction.

References
  1. Bowden, K.K., et al., Data-driven dialogue systems for social agents, in Advanced Social Interaction with Agents. 2019, Springer. p. 53-56.
  2. Salloum, S.A., et al., Using text mining techniques for extracting information from research articles, in Intelligent natural language processing: Trends and Applications. 2018, Springer. p. 373-397.
  3. Dash, A., M. Pandey, and S. Rautaray. Enhanced Entity Extraction Using Big Data Mechanics. in International Conference on Advanced Computing Networking and Informatics. 2019. Springer.
  4. Fianyi, I. and T.A. Zia, Biometric Technology Solutions to Countering Today's Terrorism, in Violent Extremism: Breakthroughs in Research and Practice. 2019, IGI Global. p. 399-412.
  5. Dong, Z.S., et al., Social media information sharing for natural disaster response. Natural Hazards, 2021: p. 1-28.
  6. Jiang, S., et al. Factoring fact-checks: Structured information extraction from fact-checking articles. in Proceedings of The Web Conference 2020. 2020.
  7. Beel, J., et al. Research paper recommender system evaluation: a quantitative literature survey. in Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation. 2013.
  8. Espadoto, M., et al., Towards a quantitative survey of dimension reduction techniques. IEEE Transactions on Visualization and Computer Graphics, 2019.
  9. Thomson, A., M. Kennelly, and K. Toohey, A systematic quantitative literature review of empirical research on large-scale sport events’ social legacies. Leisure Studies, 2020: p. 1-18.
  10. Vera-Baceta, M.-A., M. Thelwall, and K. Kousha, Web of Science and Scopus language coverage. Scientometrics, 2019. 121(3): p. 1803-1813.
  11. Wang, B., et al., An overview of climate change vulnerability: a bibliometric analysis based on Web of Science database. Natural Hazards, 2014. 74(3): p. 1649-1666.
  12. Sheth, J.N., How social media will impact marketing media, in Social media marketing. 2018, Springer. p. 3-18.
  13. Xu, W. and Y. Tan, Semi-supervised Target-oriented Sentiment Classification. Neurocomputing, 2019.
  14. Maynard, D., K. Bontcheva, and I. Augenstein, Natural Language Processing for the Semantic Web, in Synthesis Lectures on the Semantic Web: Theory and Technology. 2017, Morgan and Claypool Publishers. p. 1-196.
  15. Lim, S., C.S. Tucker, and S. Kumara, An unsupervised machine learning model for discovering latent infectious diseases using social media data. Journal of Biomedical Informatics, 2017. 66: p. 82-94.
  16. Giannakopoulos, T., et al. Interactive Text Analysis and Information Extraction. in Italian Research Conference on Digital Libraries. 2019. Springer.
  17. Ertam, F. Deep learning based text classification with Web Scraping methods. in 2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018. 2019. Institute of Electrical and Electronics Engineers Inc.
  18. Emami, H., H. Shirazi, and A.A. Barforoush, Web Person Name Disambiguation Using Social Links and Enriched Profile Information. COMPUTING AND INFORMATICS, 2019. 37(6): p. 1485-1515.
  19. Singh, A., N. Shukla, and N. Mishra, Social media data analytics to improve supply chain management in food industries. Transportation Research Part E: Logistics and Transportation Review, 2018. 114: p. 398-415.
  20. Kalra, V. and R. Agrawal, Challenges of Text Analytics in Opinion Mining, in Extracting Knowledge From Opinion Mining. 2019, IGI Global. p. 268-282.
  21. Tarasconi, F., et al. The role of unstructured data in real-time disaster-related social media monitoring. in 5th IEEE International Conference on Big Data, Big Data 2017. 2018. Institute of Electrical and Electronics Engineers Inc.
  22. Jayathilaka, K.M.P.N., A.R. Weerasinghe, and W.M.L.K.N. Wijesekara. Making sense of large volumes of unstructured email responses. in 16th International Conference on Advances in ICT for Emerging Regions, ICTer 2016. 2017. Institute of Electrical and Electronics Engineers Inc.
  23. Musial, K., et al., Extraction of Multilayered Social Networks from Activity Data. Scientific World Journal, 2014.
  24. Kang, J., A. Souili, and D. Cavallucci, Text simplification of patent documents, in 18th International TRIZ Future Conference on Automated Invention for Smart Industries, TFC 2018, S. Koziolek, D. Cavallucci, and R. De Guio, Editors. 2018, Springer New York LLC. p. 225-237.
  25. Thomas, A. and S. Sangeetha, An innovative hybrid approach for extracting named entities from unstructured text data. Computational Intelligence, 2019.
  26. Alves, T., et al., Development of text mining tools for information retrieval from patents, in 11th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2017, M. Rocha, et al., Editors. 2017, Springer Verlag. p. 66-73.
  27. Liu, Y. and Y. Huang, Research on construction of patent dynamic technology effect matrix. International Journal of Innovative Computing, Information and Control, 2018. 14(3): p. 1133-1139.
  28. Abilhoa, W.D. and L.N. de Castro, TKG: A graph-based approach to extract keywords from tweets, in 11th International Symposium on Distributed Computing and Artificial Intelligence 2014, DCAI 2014. 2014, Springer Verlag: Salamanca. p. 425-432.
  29. Sato, N. and K. Sato, Statistical analysis of word usage in biological publications since 1965: Historical delineation highlighting an emergence of function-oriented discourses in contemporary molecular and cellular biology. Journal of Theoretical Biology, 2019. 462: p. 293-303.
  30. Gutierrez, F., et al., A hybrid ontology-based information extraction system. Journal of Information Science, 2015. 42(6): p. 798-820.
  31. Ali, T., et al., Multi-model-based interactive authoring environment for creating shareable medical knowledge. Computer Methods and Programs in Biomedicine, 2017. 150: p. 41-72.
  32. Tourassi, G., et al., The utility of web mining for epidemiological research: Studying the association between parity and cancer risk. Journal of the American Medical Informatics Association, 2016. 23(3): p. 588-595.
  33. Jebbor, F. and L. Benhlima, Ontology-based enhancement of rule learning for information extraction. Journal of Theoretical and Applied Information Technology, 2018. 96(23): p. 7876-7891.
  34. Saad, S. and M.K. Mansor, Named entity recognition approach for malay crime news retrieval. GEMA Online Journal of Language Studies, 2018. 18(4): p. 216-235.
  35. Klein, A., et al. A Combined Approach for Extracting Financial Instrument-Specific Investor Sentiment from Weblogs. in Wirtschaftsinformatik. 2013.
  36. Abrantes, D. and J. Cordeiro. Extracting Adverse Drug Effects from User Experiences: A Baseline. in 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018. 2018. Institute of Electrical and Electronics Engineers Inc.
  37. Timonin, A.Y., A.S. Bozhday, and A.M. Bershadsky. Analysis of unstructured text data for a person social profile. in 2017 International Conference on Electronic Governance and Open Society, eGose 2017. 2017. Association for Computing Machinery.
  38. Tarasconi, F., et al., The Role of Unstructured Data in Real-Time Disaster-related Social Media Monitoring, in 2017 Ieee International Conference on Big Data, J.Y. Nie, et al., Editors. 2017. p. 3769-3778.
  39. Bin Tareaf, R., et al., Personality Exploration System for Online Social Networks: Facebook Brands As a Use Case. 2018 Ieee/Wic/Acm International Conference on Web Intelligence. 2018. 301-309.
  40. Abdelsadek, Y., et al., Community extraction and visualization in social networks applied to Twitter. Information Sciences, 2018. 424: p. 204-223.
  41. Beigi, G., et al., An overview of sentiment analysis in social media and its applications in disaster relief, in Sentiment analysis and ontology engineering. 2016, Springer. p. 313-340.
  42. Pandey, N. and S. Natarajan, How Social Media can Contribute during Disaster Events? 2016 International Conference on Advances in Computing, Communications and Informatics, ed. J. Wu, et al. 2016. 1352-1356.
  43. Luong, T.L., et al. Intent extraction from social media texts using sequential segmentation and deep learning models. in 9th International Conference on Knowledge and Systems Engineering, KSE 2017. 2017. Institute of Electrical and Electronics Engineers Inc.
  44. Ramírez-Cifuentes, D., M. Mayans, and A. Freire, Early risk detection of anorexia on social media, in 5th International Conference on Internet Science, INSCI 2018, S.S. Bodrunova, Editor. 2018, Springer Verlag. p. 3-14.
  45. Nikfarjam, A., et al., Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. Journal of the American Medical Informatics Association, 2015. 22(3): p. 671-681.
  46. Yang, T.F., et al., Social Media Big Data Mining and Spatio-Temporal Analysis on Public Emotions for Disaster Mitigation. Isprs International Journal of Geo-Information, 2019. 8(1).
  47. Sarker, A., A. Nikfarjam, and G. Gonzalez, Social media mining shared task workshop. 21st Pacific Symposium on Biocomputing, PSB 2016, 2016: p. 581-592.
  48. Aung, S.S. and M.S. Wai, Domain independent feature extraction using rule based approach. Advances in Science, Technology and Engineering Systems, 2018. 3(1): p. 218-224.
  49. Žižka, J. and F. Dařena. Revealing potential changes of significant terms in streams of textual data written in natural languages using windowing and text mining. in Artificial Intelligence and Natural Language and Information Extraction, Social Media and Web Search FRUCT Conference, AINL-ISMW FRUCT 2015. 2016. Institute of Electrical and Electronics Engineers Inc.
  50. Sparks, K.A., et al. Facility detection and popularity assessment from text classification of social media and crowdsourced data. in 10th Workshop on Geographic Information Retrieval, GIR 2016. 2016. Association for Computing Machinery, Inc.
  51. Sang, L., et al., WEFEST: Word Embedding Feature Extension for Short Text Classification, in 2016 Ieee 16th International Conference on Data Mining Workshops, C. Domeniconi, et al., Editors. 2016. p. 677-683.
  52. Fianyi, I. and T. Zia, Biometric technology solutions to countering today’s terrorism, in Violent Extremism: Breakthroughs in Research and Practice. 2018, IGI Global. p. 399-412.
  53. Papadimitriou, D., et al., Goals in Social Media, Information Retrieval and Intelligent Agents, in 2015 Ieee 31st International Conference on Data Engineering. 2015. p. 1538-1540.
  54. Onal, K.D. and P. Karagoz. Named entity recognition from scratch on social media. in 6th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2015. 2015. CEUR-WS.
  55. Fabian, B., A. Baumann, and M. Keil. Privacy on reddit? Towards large-scale user classification. in 23rd European Conference on Information Systems, ECIS 2015. 2015. Association for Information Systems.
  56. Zolnoori, M., et al., A systematic approach for developing a corpus of patient reported adverse drug events: A case study for SSRI and SNRI medications. Journal of Biomedical Informatics, 2019. 90.
  57. Saad Missen, M.M., et al., SentiML ++: an extension of the SentiML sentiment annotation scheme. New Review of Hypermedia and Multimedia, 2018. 24(1): p. 28-43.
  58. Funk, C., et al. Community-driven crowdsourcing: Data collection with local developers. in 11th International Conference on Language Resources and Evaluation, LREC 2018. 2019. European Language Resources Association (ELRA).
  59. Chen, Y.F., et al., Review on Rumor Detection of Online Social Networks. Jisuanji Xuebao/Chinese Journal of Computers, 2018. 41(7): p. 1648-1677.
  60. Basile, V., M. Lai, and M. Sanguinetti. Long-term social media data collection at the University of Turin. in 5th Italian Conference on Computational Linguistics, CLiC-it 2018. 2018. CEUR-WS.
  61. Abid, A., et al. An extraction and unification methodology for social networks data: An application to public security. in 19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017. 2017. Association for Computing Machinery.
  62. Zaghouani, W. and A. Charfi. AraP-Tweet: A large multi-dialect twitter corpus for gender, age and language variety identification. in 11th International Conference on Language Resources and Evaluation, LREC 2018. 2019. European Language Resources Association (ELRA).
  63. Kuwertz, A., et al., Applying knowledge-based reasoning for information fusion in intelligence, surveillance, and reconnaissance, in 13th IEEE International Conference on Multisensor Integration and Fusion, IEEE MFI 2017, H. Ko, S. Lee, and S. Oh, Editors. 2018, Springer Verlag. p. 119-139.
  64. Farnadi, G., et al. User profiling through deep multimodal fusion. in 11th ACM International Conference on Web Search and Data Mining, WSDM 2018. 2018. Association for Computing Machinery, Inc.
  65. Zhang, S., et al., Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR), 2019. 52(1): p. 5.
  66. Liu, T., et al., Deep learning-based super-resolution in coherent imaging systems. Scientific Reports, 2019. 9(1).
  67. Zhou, K., et al., Deep sentiment hashing for text retrieval in social CIoT. Future Generation Computer Systems, 2018. 86: p. 362-371.
  68. Zhang, X., et al., Exploring Deep Recurrent Convolution Neural Networks for Subjectivity Classification. IEEE Access, 2019. 7: p. 347-357.
  69. Liu, B., Sentiment analysis: Mining opinions, sentiments, and emotions. 2015: Cambridge University Press.
  70. Mao, W., W. Feng, and X. Liang, A novel deep output kernel learning method for bearing fault structural diagnosis. Mechanical Systems and Signal Processing, 2019. 117: p. 293-318.
  71. Coates, A., A. Ng, and H. Lee. An analysis of single-layer networks in unsupervised feature learning. in Proceedings of the fourteenth international conference on artificial intelligence and statistics. 2011.
  72. Marcus, G., Deep learning: A critical appraisal. arXiv preprint arXiv:1801.00631, 2018.
  73. Bengio, Y., Learning deep architectures for AI. Foundations and trends® in Machine Learning, 2009. 2(1): p. 1-127.
  74. Scherer, R., Feature detection, in Studies in Computational Intelligence. 2020, Springer Verlag. p. 7-32.
  75. Kamruzzaman, A., Y. Alhwaiti, and C.C. Tappert, Developing a deep learning model to implement rosenblatt’s experiential memory brain model, in Lecture Notes in Networks and Systems. 2020, Springer. p. 248-261.
  76. Zhao, Z., et al. Deep neural network based protein-protein interaction extraction from biomedical literature. in IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. 2015. Institute of Electrical and Electronics Engineers Inc.
  77. Agnati, L.F., et al., The brain as a “hyper-network”: the key role of neural networks as main producers of the integrated brain actions especially via the “broadcasted” neuroconnectomics. Journal of Neural Transmission, 2018. 125(6): p. 1-15.
  78. Qi, Y., et al., Deep learning for character-based information extraction, in 36th European Conference on Information Retrieval, ECIR 2014. 2014, Springer Verlag: Amsterdam. p. 668-674.
  79. Jin, Y., D. Wu, and W. Guo, Attention-Based LSTM with Filter Mechanism for Entity Relation Classification. Symmetry, 2020. 12(10): p. 1729.
  80. Liu, G. and J. Guo, Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing, 2019. 337: p. 325-338.
  81. Li, W., D. Guo, and X. Fang, Multimodal architecture for video captioning with memory networks and an attention mechanism. Pattern Recognition Letters, 2018. 105: p. 23-29.
  82. Wu, X., et al., Conditional BERT Contextual Augmentation, J.M.F. Rodrigues, et al., Editors. 2019, Springer Verlag. p. 84-95.
  83. Zirikly, A. and M. Diab, ANEAR: Automatic named entity aliasing resolution, in 18th International Conference on Application of Natural Language to Information Systems, NLDB 2013. 2013: Salford. p. 213-224.
  84. Zhou, X., et al. Fake News: Fundamental theories, detection strategies and challenges. in 12th ACM International Conference on Web Search and Data Mining, WSDM 2019. 2019. Association for Computing Machinery, Inc.
  85. Zhang, Y., et al., Empower event detection with bi-directional neural language model. Knowledge-Based Systems, 2019. 167: p. 87-97.
  86. Zaw, M. and P. Tandayya. Multi-level Sentiment Information Extraction Using the CRbSA Algorithm. in 15th International Joint Conference on Computer Science and Software Engineering, JCSSE 2018. 2018. Institute of Electrical and Electronics Engineers Inc.
  87. Zahra, K., et al. A framework for user characterization based on tweets using machine learning algorithms. in 7th International Conference on Network, Communication and Computing, ICNCC 2018. 2018. Association for Computing Machinery.
  88. Yu, Z., et al., Improving the utility of MeSH® terms using the TopicalMeSH representation. Journal of Biomedical Informatics, 2016. 61: p. 77-86.
  89. Yin, Z., et al., A scalable framework to detect personal health mentions on Twitter. Journal of Medical Internet Research, 2015. 17(6): p. e138.
  90. Yang, Q., et al., Building enhanced link context by logical sitemap, in 6th International Conference on Knowledge Science, Engineering and Management, KSEM 2013. 2013: Dalian. p. 36-47.
  91. Xu, A., et al. A new chatbot for customer service on social media. in 2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017. 2017. Association for Computing Machinery.
  92. Wu, Y., et al., Sequential matching network: A new architecture for multi-turn response selection in retrieval-based chatbots. arXiv preprint arXiv:1612.01627, 2016.
  93. Weitzel, L., R.C. Prati, and R.F. Aguiar, The comprehension of figurative language: What is the influence of irony and sarcasm on NLP techniques?, in Studies in Computational Intelligence. 2016, Springer Verlag. p. 49-74.
  94. Wang, W., et al. A multiple instance learning framework for identifying key sentences and detecting events. in 25th ACM International Conference on Information and Knowledge Management, CIKM 2016. 2016. Association for Computing Machinery.
  95. Trusov, R., et al. Multi-representation approach to text regression of financial risks. in Artificial Intelligence and Natural Language and Information Extraction, Social Media and Web Search FRUCT Conference, AINL-ISMW FRUCT 2015. 2016. Institute of Electrical and Electronics Engineers Inc.
  96. Tian, T. and W. Xu, Chinese lexical normalization based on information extraction: An experimental study, in 26th International Conference on Artificial Neural Networks, ICANN 2017, A. Lintas, et al., Editors. 2017, Springer Verlag. p. 216-223.
  97. Tian, B. and C. Xing, Deep Learning Based Temporal Information Extraction Framework on Chinese Electronic Health Records, X. Wang, et al., Editors. 2018, Springer Verlag. p. 203-214.
  98. Thomas, P., et al. Streaming text analytics for real-time event recognition. in 11th International Conference on Recent Advances in Natural Language Processing, RANLP 2017. 2017. Association for Computational Linguistics (ACL).
  99. Srivastava, S.K., S.K. Singh, and J.S. Suri, Effect of incremental feature enrichment on healthcare text classification system: A machine learning paradigm. Computer Methods and Programs in Biomedicine, 2019. 172: p. 35-51.
  100. Sohangir, S. and D. Wang. Finding expert authors in financial forum using deep learning methods. in 2nd IEEE International Conference on Robotic Computing, IRC 2018. 2018. Institute of Electrical and Electronics Engineers Inc.
  101. Li, L., L. Jin, and D. Huang, Exploring recurrent neural networks to detect named entities from biomedical text, M. Sun, et al., Editors. 2015, Springer Verlag. p. 279-290.
  102. Wang, W.Y., J. Li, and X. He. Deep reinforcement learning for NLP. in 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018. 2018. Association for Computational Linguistics (ACL).
  103. Goldberg, Y., Neural network methods for natural language processing. Computational Linguistics, 2018. 44(1): p. 194-195.
  104. Viani, N., et al., Supervised methods to extract clinical events from cardiology reports in Italian. Journal of Biomedical Informatics, 2019. 95.
  105. Gupta, S., et al., Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction mention extraction. BMC Bioinformatics, 2018. 19.
  106. Gao, S., et al., Hierarchical attention networks for information extraction from cancer pathology reports. Journal of the American Medical Informatics Association, 2018. 25(3): p. 321-330.
  107. Xiang, X., et al., Skeleton to abstraction: An attentive information extraction schema for enhancing the saliency of text summarization. Information (Switzerland), 2018. 9(9).
  108. Liu, S., Y. Li, and B. Fan, Hierarchical RNN for few-shot information extraction learning, Q. Zhou, et al., Editors. 2018, Springer Verlag. p. 227-239.
  109. Yin, Z., K.H. Chang, and R. Zhang. DeepProbe: Information directed sequence understanding and chatbot design via recurrent neural networks. 2017. Association for Computing Machinery.
  110. Banerjee, I., et al., Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification. Artificial Intelligence in Medicine, 2019. 97: p. 79-88.
  111. Banik, N. and M.H.H. Rahman. GRU based Named Entity Recognition System for Bangla Online Newspapers. 2019. Institute of Electrical and Electronics Engineers Inc.
  112. Zhang, C., et al., Multi-Gram CNN-Based Self-Attention Model for Relation Classification. IEEE Access, 2019. 7: p. 5343-5357.
  113. Chowdhury, S., et al., A multitask bi-directional RNN model for named entity recognition on Chinese electronic medical records. BMC Bioinformatics, 2018. 19.
  114. Nagesh Bhattu, S., N. Satya Krishna, and D.V.L.N. Somayajulu. Idrbt-team-a@IECSIL-FIRE-2018: Named Entity Recognition of Indian languages using Bi-LSTM. 2018. CEUR-WS.
  115. Lyu, C., et al., Long short-term memory RNN for biomedical named entity recognition. BMC Bioinformatics, 2017. 18(1).
  116. Prabha, G., et al. A Deep Learning Approach for Part-of-Speech Tagging in Nepali Language. 2018. Institute of Electrical and Electronics Engineers Inc.
  117. Jung, S. and C. Lee, Deep Neural Architecture for Recovering Dropped Pronouns in Korean. ETRI Journal, 2018. 40(2): p. 257-265.
  118. Hu, B., et al. Convolutional neural network architectures for matching natural language sentences. in Advances in neural information processing systems. 2014.
  119. Chen, W.F. and L.W. Ku. UTCNN: A deep learning model of stance classification on social media text. in 26th International Conference on Computational Linguistics, COLING 2016. 2016. Association for Computational Linguistics, ACL Anthology.
  120. Khalifa, M. and K. Shaalan, Character convolutions for Arabic Named Entity Recognition with Long Short-Term Memory Networks. Computer Speech and Language, 2019. 58: p. 335-346.
  121. Xu, H., et al., Research on Chinese Nested Named Entity Relation Extraction. Beijing Daxue Xuebao (Ziran Kexue Ban)/Acta Scientiarum Naturalium Universitatis Pekinensis, 2019. 55(1): p. 8-14.
  122. Ru, C., et al. Syntactic representation learning for open information extraction on web. 2019. International World Wide Web Conferences Steering Committee.
  123. Ren, X., et al. A novel text structure feature extractor for Chinese scene text detection and recognition. 2017. Institute of Electrical and Electronics Engineers Inc.
  124. Qiu, J.X., et al., Scalable deep text comprehension for Cancer surveillance on high-performance computing. BMC Bioinformatics, 2018. 19.
  125. Nagarajan, S. and K. Perumal. A deep neural network for information extraction from web pages. 2018. Institute of Electrical and Electronics Engineers Inc.
  126. Zhang, C., et al., CNN-VWII: An efficient approach for large-scale video retrieval by image queries. Pattern Recognition Letters, 2019. 123: p. 82-88.
  127. Arras, L., et al., "What is relevant in a text document?": An interpretable machine learning approach. PLoS ONE, 2017. 12(8).
  128. Yoon, H.J., et al. Filter pruning of Convolutional Neural Networks for text classification: A case study of cancer pathology report comprehension. 2018. Institute of Electrical and Electronics Engineers Inc.
  129. Alawad, M., H.J. Yoon, and G.D. Tourassi. Coarse-to-fine multi-task training of convolutional neural networks for automated information extraction from cancer pathology reports. 2018. Institute of Electrical and Electronics Engineers Inc.
  130. Conway, L.G., K.R. Conway, and S.C. Houck, Validating automated integrative complexity: Natural language processing and the Donald Trump test. Journal of Social and Political Psychology, 2020. 8(2): p. 504-524.
Index Terms

Computer Science
Information Sciences

Keywords

Deep learning Algorithms Natural Language Processing Information Extraction Social Networks

Powered by PhDFocusTM