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Data-centric AI has shed light on the significance of data within the machine learning (ML) pipeline.
Data cleaning: Problems and current approaches
Rahm, E., Do, H. H., et al · 2000
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Applications of data mining in retail business
Ahmed, S. R · 2004
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Crawling the web
Pant, G., Srinivasan, P., and Menczer, F · 2004
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Ethical issues in web data mining
Van Wel, L. and Royakkers, L · 2004
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Named entity recognition approaches
Mansouri, A., Affendey, L. S., and Mamat, A · 2008
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Information extraction
Hobbs, J. R. and Riloff, E · 2010
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Ethics beyond the irb: An introductory essay
Blee, K. M. and Currier, A · 2011
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Semi-supervised consensus labeling for crowdsourcing
Tang, W. and Lease, M · 2011
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Towards an integrated crowdsourcing definition
Estellés-Arolas, E. and González-Ladrón-de Guevara, F · 2012
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European soil data centre: Response to european policy support and public data requirements
Panagos, P., Van Liedekerke, M., Jones, A., and Montanarella, L · 2012
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Natural Language Annotation for Machine Learning: A guide to corpus-building for applications
Pustejovsky, J. and Stubbs, A · 2012
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Inter-annotator agreement for dependency annotation of learner language
Ragheb, M. and Dickinson, M · 2013
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Contingent workers: Workers’ compensation data analysis strategies and limitations
Foley, M., Ruser, J., Shor, G., Shuford, H., and Sygnatur, E · 2014
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Crowdsourcing: a comprehensive literature review
Hossain, M. and Kauranen, I · 2015
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Open data licensing: More than meets the eye
Khayyat, M. and Bannister, F · 2015
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Squad: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
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Text punctuation: an inter-annotator agreement study
Boháč, M., Rott, M., and Kovář, V · 2017
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Recent advances in document summarization
Yao, J.-g., Wan, X., and Xiao, J · 2017
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Human-in-the-loop data analysis: a personal perspective
Doan, A · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R · 2018
Cited alongside, same era.
Overview of the germeval 2018 shared task on the identification of offensive language
Wiegand, M., Siegel, M., and Ruppenhofer, J · 2018
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A system to quantify industrial data quality
Goosen, A. E · 2019
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A review on data cleansing methods for big data
Ridzuan, F. and Zainon, W. M. N. W · 2019
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A survey on data collection for machine learning: a big data-ai integration perspective
Enterprise ai canvas integrating artificial intelligence into business
Kerzel, U · 2021
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Datasets: A community library for natural language processing
Lhoest, Q., del Moral, A. V., Jernite, Y., Thakur, A., von Platen, P., Patil, S., Chaumond, J., Drame, M., Plu, J., Tunstall, L., et al · 2021
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A large-scale dataset for benchmarking elevator button segmentation and character recognition
Liu, J., Fang, Y., Zhu, D., Ma, N., Pan, J., and Meng, M. Q.-H · 2021
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Filter-mbart based neural machine translation using parallel corpus filtering
Moon, H., Park, C., Eo, S., Park, J., and Lim, H · 2021
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Park, C., Lee, S., Moon, H., Eo, S., Seo, J., and Lim, H · 2021
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Roh, Y., Heo, G., and Whang, S. E · 2019
Cited alongside, same era.
A human-centered wearable sensing platform with intelligent automated data annotation capabilities
Solis, R., Pakbin, A., Akbari, A., Mortazavi, B. J., and Jafari, R · 2019
Cited alongside, same era.
Using ai to enhance business operations
Tarafdar, M., Beath, C. M., and Ross, J. W · 2019
Cited alongside, same era.
Tacred revisited: A thorough evaluation of the tacred relation extraction task
Alt, C., Gabryszak, A., and Hennig, L · 2020
Cited alongside, same era.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Cited alongside, same era.
Research perspectives: the anatomy of a design principle
Gregor, S., Chandra Kruse, L., Seidel, S., et al · 2020
Cited alongside, same era.
Eras: Improving the quality control in the annotation process for natural language processing tasks
Grosman, J. S., Furtado, P. H., Rodrigues, A. M., Schardong, G. G., Barbosa, S. D., and Lopes, H. C · 2020
Cited alongside, same era.
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Lighttag: Text annotation platform
Perry, T · 2021
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What can data-centric ai learn from data and ml engineering?
Polyzotis, N. and Zaharia, M · 2021
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Challenges and opportunities in nlp benchmarking, 2021
Ruder, S · 2021
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Sustainable industrial and operation engineering trends and challenges toward industry 4.0: A data driven analysis
Tseng, M.-L., Tran, T. P. T., Ha, H. M., Bui, T.-D., and Lim, M. K · 2021
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Can artificial intelligence transform devops?
Alenezi, M., Zarour, M., and Akour, M · 2022
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No language left behind: Scaling human-centered machine translation
Costa-jussà, M. R., Cross, J., Çelebi, O., Elbayad, M., Heafield, K., Heffernan, K., Kalbassi, E., Lam, J., Licht, D., Maillard, J., et al · 2022
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Generate, annotate, and learn: Nlp with synthetic text
He, X., Nassar, I., Kiros, J., Haffari, G., and Norouzi, M · 2022
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K-nct: Korean neural grammatical error correction gold-standard test set using novel error type classification criteria
Koo, S., Park, C., Seo, J., Lee, S., Moon, H., Lee, J., and Lim, H · 2022
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How to explain ai systems to end users: a systematic literature review and research agenda
Laato, S., Tiainen, M., Islam, A. N., and Mäntymäki, M · 2022
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Dataperf: Benchmarks for data-centric ai development
Mazumder, M., Banbury, C., Yao, X., Karlaš, B., Rojas, W. G., Diamos, S., Diamos, G., He, L., Kiela, D., Jurado, D., et al · 2022
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The asr post-processor performance challenges of backtranscription (bts): Data-centric and model-centric approaches
Park, C., Seo, J., Lee, S., Lee, C., and Lim, H · 2022
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Potato: The portable text annotation tool
Pei, J., Ananthasubramaniam, A., Wang, X., Zhou, N., Sargent, J., Dedeloudis, A., and Jurgens, D · 2022
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A survey of human-in-the-loop for machine learning
Wu, X., Xiao, L., Sun, Y., Zhang, J., Ma, T., and He, L · 2022
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