Dr Matthew Shardlow
Senior Lecturer
My profile
Biography
As a lecturer in the Department of Computing and Mathematics, my time is split between teaching and research. In my role as a teacher I lead the first year Introduction to Programming unit for the Digital and Technology Solutions Degree Apprenticeship. In this unit I teach a varied group of around 150 students how to program using the Java programming language. In my role as a researcher I have 3 key interests that I pursue, all themed around natural language processing. Firstly, I am interested in text simplification, a field that looks at taking a complex text and making it easier to read for an end user. Secondly, I’m interested in how we use Emoji as part of everyday language and how we can teach machines to better understand emoji. Thirdly, I’m interested in The application of Text Mining to varied disciplines which have included: Chemistry, Neuroscience, Journalism and Finance.
Academic and professional qualifications
I studied at the University of Manchester from 2007 to 2015, completing my BSc and PhD. In My PhD, I focussed on the topic of lexical simplification and published several academic articles, as well as my thesis. Following on from my PhD, I worked as part of an EC H2020 project called “An Open Mining Infrastructure for Text and Data (OpenMinTeD)” at the National Centre for Text Mining. In this role I helped develop a text mining platform that is available for use by non-expert users. In 2017, I moved to Manchester Metropolitan University to take up the role of lecturer. In this role I am pursuing my own avenues of research, whilst also maintaining and developing existing research connections.
Teaching
Why do I teach?
I see my teaching as an extension of my research. If I did not research, I would have nothing to teach. I am interested in communicating the results of my research and the cutting edge of the research fields that I am interested in to students in my tutelage. To this end, I am happy to take on keenly motivated students whose research interests align with mine at BSc, Masters and PhD level. If you are interested in doing a project with me, please get in touch and I would be happy to discuss.
Why study…
Programming
Learning to program is a vital skill for the technologist in the modern age. Even roles that do not directly require day-to-day programming will benefit from an understanding of the effort that goes in to programming. Learning to program means that you better understand how the software that you use every day works and will give you the ability to write software for yourself that will help you by automating day to day taskes
Natural Language Processing
Have you ever used Siri? Or Google? Or a bot on Whatsapp or Messenger? Then you have interacted with Natural Language Processing (NLP) algorithms. NLP enables machines to understand language, ranging from the basic level of identifying words, sentences, etc. To more complex features such as understanding the sentiment of a text or understanding different meanings of words.
Subject areas
Introduction to Programming, Natural Language Processing
Research outputs
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Reports
Shardlow, M., Latham, A. (2023) ChatGPT in computing education: a policy whitepaper. UK: Council of Professors and Heads of Computing.
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Journal articles
Li, Z., Shardlow, M. (2024) 'How do control tokens affect natural language generation tasks like text simplification.' Natural Language Engineering,
Shardlow, M. (2023) 'Can A Language Model Be Conscious?.' ITNOW, 65(3) pp. 54-55.
Afzal, M.K., Shardlow, M., Tuarob, S., Zaman, F., Sarwar, R., Ali, M., Aljohani, N.R., Lytras, M.D., Nawaz, R., Hassan, S.U. (2023) 'Generative image captioning in Urdu using deep learning.' Journal of Ambient Intelligence and Humanized Computing, 14(6) pp. 7719-7731.
North, K., Zampieri, M., Shardlow, M. (2023) 'Lexical complexity prediction: an overview.' ACM Computing Surveys, 55(9) pp. 179-179.
Štajner, S., Ferrés, D., Shardlow, M., North, K., Zampieri, M., Saggion, H. (2022) 'Lexical simplification benchmarks for English, Portuguese, and Spanish.' Frontiers in Artificial Intelligence, 5pp. 991242-991242.
Shardlow, M., Sellar, S., Rousell, D. (2022) 'Collaborative augmentation and simplification of text (CoAST): pedagogical applications of natural language processing in digital learning environments.' Learning Environments Research, 25(2) pp. 399-421.
Shardlow, M., Evans, R., Zampieri, M. (2022) 'Predicting lexical complexity in English texts: the Complex 2.0 dataset.' Language Resources and Evaluation, 56(4) pp. 1153-1194.
Shardlow, M., Gerber, L., Nawaz, R. (2022) 'One emoji, many meanings: A corpus for the prediction and disambiguation of emoji sense.' Expert Systems with Applications, 198
Nawaz, R., Sun, Q., Shardlow, M., Kontonatsios, G., Aljohani, N.R., Visvizi, A., Hassan, S.U. (2022) 'Leveraging AI and Machine Learning for National Student Survey: Actionable Insights from Textual Feedback to Enhance Quality of Teaching and Learning in UK’s Higher Education.' Applied Sciences, 12(1) pp. 514-514.
Zaman, F., Shardlow, M., Hassan, S.U., Aljohani, N.R., Nawaz, R. (2020) 'HTSS: A novel hybrid text summarisation and simplification architecture.' Information Processing & Management, 57(6)
Shardlow, M., Ju, M., Li, M., O’Reilly, C., Iavarone, E., McNaught, J., Ananiadou, S. (2018) 'A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience.' Neuroinformatics, 17(3) pp. 391-406.
Shardlow, M., Batista-Navarro, R., Thompson, P., Nawaz, R., McNaught, J., Ananiadou, S. (2018) 'Identification of research hypotheses and new knowledge from scientific literature.' BMC Medical Informatics and Decision Making, 18pp. 1-13.
Korkontzelos, I., Nikfarjam, A., Shardlow, M., Sarker, A., Ananiadou, S., Gonzalez, G.H. (2016) 'Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts.' Journal of Biomedical Informatics, 62pp. 148-158.
Piotr, P., Shardlow, M.J., Sophie, A., Robert, B., Richard, E.D.C., Stelios, P., John, M., Sophia, A. (2016) 'Text mining resources for the life sciences.' Database : the Journal of Biological Databases and Curation,
Przybyla, P., Shardlow, M., Aubin, S., Bossy, R., De Castilho, R.E., Piperidis, S., McNaught, J., Ananiadou, S. (2016) 'Text mining resources for the life sciences.' Database, 2016pp. 1-30.
Shardlow, M. (2014) 'A Survey of Automated Text Simplification.' International Journal of Advanced Computer Science and Applications, 4(1)
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Conference papers
Goldsack, T., Luo, Z., Xie, Q., Scarton, C., Shardlow, M., Ananiadou, S., Lin, C. (2023) 'Overview of the BioLaySumm 2023 Shared Task on Lay Summarization of Biomedical Research Articles.' pp. 468-477.
North, K., Dmonte, A., Ranasinghe, T., Shardlow, M., Zampieri, M. (2023) 'ALEXSIS+: Improving Substitute Generation and Selection for Lexical Simplification with Information Retrieval.' pp. 404-413.
Williams, A., Shardlow, M. (2022) 'Extending a corpus for assessing the credibility of software practitioner blog articles using meta-knowledge.' In EASE 2022: The International Conference on Evaluation and Assessment in Software Engineering. Gothenburg Sweden, 13/6/2022 - 15/6/2022. Staron, M., Berger, C., Simmonds, J., Prikladnicki, R. (ed.) Association for Computing Machinery (ACM), pp. 305-310.
Przybyła, P., Shardlow, M. (2022) 'Using NLP to quantify the environmental cost and diversity benefits of in-person NLP conferences.' In 60th Annual Meeting of the Association for Computational Linguistics. Dublin, Republic of Ireland, 22/5/2022 - 27/5/2022. Muresan, S., Nakov, P., Villavicencio, A. (ed.) Association for Computational Linguistics, pp. 3853-3863.
Shardlow, M., Alva-Manchego, F. (2022) 'Simple TICO-19: A Dataset for Joint Translation and Simplification of COVID-19 Texts.' pp. 3093-3102.
Shardlow, M. (2022) 'Agree to Disagree: Exploring Subjectivity in Lexical Complexity.' pp. 9-16.
Alva-Manchego, F., Shardlow, M. (2022) 'Towards Readability-Controlled Machine Translation of COVID-19 Texts.' pp. 287-288.
North, K., Zampieri, M., Shardlow, M. (2022) 'An Evaluation of Binary Comparative Lexical Complexity Models.' pp. 197-203.
Vásquez-Rodríguez, L., Shardlow, M., Przybyła, P., Ananiadou, S. (2021) 'The role of Text Simplification operations in evaluation.' In First Workshop on Current Trends in Text Simplification (CTTS 2021) co-located with the 37th Conference of the Spanish Society for Natural Language Processing (SEPLN2021). 2944. Online (initially located in Málaga, Spain), 21/9/2021 - 21/9/2021. Saggion, H., Štajner, S., Ferrés, D., Sheang, K.C. (ed.) CEUR-WS, pp. 57-69.
Williams, A., Shardlow, M., Rainer, A. (2021) 'Towards a corpus for credibility assessment in software practitioner blog articles.' In EASE 2021: Evaluation and Assessment in Software Engineering. Trondheim, Norway, 21/6/2021 - 23/6/2021. Chitchyan, R., Li, J. (ed.) New York: Association for Computing Machinery (ACM), pp. 100-108.
Vásquez-Rodríguez, L., Shardlow, M., Przybyla, P., Ananiadou, S. (2021) 'Investigating Text Simplification Evaluation.' pp. 876-882.
Shardlow, M., Evans, R., Paetzold, G.H., Zampieri, M. (2021) 'SemEval-2021 Task 1: Lexical Complexity Prediction.' pp. 1-16.
Flynn, R., Shardlow, M. (2021) 'Manchester Metropolitan at SemEval-2021 Task 1: Convolutional Networks for Complex Word Identification.' pp. 603-608.
Cooper, M., Shardlow, M. (2020) 'CombiNMT: An exploration into neural text simplification models.' pp. 5588-5594.
Kochmar, E., Gooding, S., Shardlow, M. (2020) 'Detecting multiword expression type helps lexical complexity assessment.' pp. 4426-4435.
Przybyła, P., Shardlow, M. (2020) 'Multi-Word Lexical Simplification.' pp. 1435-1446.
Mohammad, S., Khan, M.U.S., Ali, M., Liu, L., Shardlow, M., Nawaz, R. (2019) 'Bot detection using a single post on social media.' 30/7/2019 - 31/7/2019. IEEE,
Shardlow, M., Nawaz, R. (2019) 'Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table.' 29/7/2019 - 31/7/2019. Association for Computatioal Linguistics (ACL),
Khalid, S., Ul Hassan, S., Shardlow, M., Dancey, D., Nawaz, R. (2019) 'Author Name Disambiguation on Ambiguous Data of Chinese Authors using Machine Learning Approaches.' In Conference on Smart Information & Communication Technologies. 26/9/2019 - 28/9/2019.
Luciano, G., Shardlow, M.J. (2018) 'Manchester Metropolitan at SemEval-2018 Task 2: Random Forest with an Ensemble of Features for Predicting Emoji in Tweets.' 5/6/2018 - 6/6/2018. Association for Computational Linguistics (ACL), pp. 491-496.
Shardlow, M.J., Nguyen, N., Owen, G., O'Donovan, C., Leach, A., McNaught, J., Turner, S., Ananiadou, S. (2018) 'A New Corpus to Support Text Mining for the Curation of Metabolites in the ChEBI Database.' In Eleventh International Conference on Language Resources and Evaluation (LREC 2018). Miyazaki, Japan, 7/5/2018 - 12/5/2018. pp. 280-285.
Piotr, P., Nhung, N., Shardlow, M.J., Georgios, K., Sophia, A. (2016) 'NaCTeM at SemEval-2016 Task 1: Inferring sentence-level semantic similarity from an ensemble of complementary lexical and sentence-level features.' In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016).
Shardlow, M.J. (2014) 'Out in the Open: Finding and Categorising Errors in the Lexical Simplification Pipeline.' In Language Resources and Evaluation Conference (LREC).
Shardlow, M.J. (2013) 'A Comparison of Techniques to Automatically Identify Complex Words.' In 51st Annual Meeting of the Association for Computational Linguistics Proceedings of the Student Research Workshop.
Shardlow, M.J. (2013) 'The CW Corpus: A New Resource for Evaluating the Identification of Complex Words.' In Proceedings of the Second Workshop on Predicting and Improving Text Readability for Target Reader Populations.
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Presentations
Shardlow, M., Gerber, L. (2019) Towards an Emoji WordNet. [Presentation] Advances in Data Science 2019 Conference, Data Science Institute, University of Manchester, Manchester,20/5/2019.
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Theses and dissertations
Shardlow, M.J. (2015) Lexical Simplification: Optimising the Pipeline.