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.
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.
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.
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.
Z. Li, M. Shardlow (2024). How do control tokens affect natural language generation tasks like text simplification. Natural Language Engineering. pp.1-28.
M. Shardlow, S. Sellar, D. Rousell (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.
M. Shardlow, M. Ju, M. Li, C. O’Reilly, E. Iavarone, et al. J. McNaught, S. Ananiadou. (2019). A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience. Neuroinformatics. 17(3), pp.391-406.
I. Korkontzelos, A. Nikfarjam, M. Shardlow, A. Sarker, S. Ananiadou, et al. GH. Gonzalez. (2016). Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts. Journal of Biomedical Informatics. 62, pp.148-158.
P. Przybyla, M. Shardlow, S. Aubin, R. Bossy, RE. De Castilho, et al. S. Piperidis, J. McNaught, S. Ananiadou. (2016). Text mining resources for the life sciences. Database. 2016, pp.1-30.
M. Shardlow A Survey of Automated Text Simplification. International Journal of Advanced Computer Science and Applications. 4(1),
K. North, A. Dmonte, T. Ranasinghe, M. Shardlow, M. Zampieri (2023). ALEXSIS+: Improving Substitute Generation and Selection for Lexical Simplification with Information Retrieval. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics. pp.404-413.
T. Goldsack, Z. Luo, Q. Xie, C. Scarton, M. Shardlow, et al. S. Ananiadou, C. Lin. (2023). Overview of the BioLaySumm 2023 Shared Task on Lay Summarization of Biomedical Research Articles. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics. pp.468-477.
M. Shardlow (2022). Agree to Disagree: Exploring Subjectivity in Lexical Complexity. In: 2nd Workshop on Tools and Resources for REAding DIfficulties, READI 2022 - collocated with the International Conference on Language Resources and Evaluation Conference, LREC 2022. pp.9-16.
M. Shardlow, F. Alva-Manchego (2022). Simple TICO-19: A Dataset for Joint Translation and Simplification of COVID-19 Texts. In: 2022 Language Resources and Evaluation Conference, LREC 2022. pp.3093-3102.
F. Alva-Manchego, M. Shardlow (2022). Towards Readability-Controlled Machine Translation of COVID-19 Texts. In: EAMT 2022 - Proceedings of the 23rd Annual Conference of the European Association for Machine Translation. pp.287-288.
K. North, M. Zampieri, M. Shardlow (2022). An Evaluation of Binary Comparative Lexical Complexity Models. In: BEA 2022 - 17th Workshop on Innovative Use of NLP for Building Educational Applications, Proceedings. pp.197-203.
R. Flynn, M. Shardlow (2021). Manchester Metropolitan at SemEval-2021 Task 1: Convolutional Networks for Complex Word Identification. In: SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop. pp.603-608.
M. Shardlow, R. Evans, GH. Paetzold, M. Zampieri (2021). SemEval-2021 Task 1: Lexical Complexity Prediction. In: SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop. pp.1-16.
L. Vásquez-Rodríguez, M. Shardlow, P. Przybyla, S. Ananiadou (2021). Investigating Text Simplification Evaluation. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. pp.876-882.
E. Kochmar, S. Gooding, M. Shardlow (2020). Detecting multiword expression type helps lexical complexity assessment. In: LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings. pp.4426-4435.
M. Cooper, M. Shardlow (2020). CombiNMT: An exploration into neural text simplification models. In: LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings. pp.5588-5594.
P. Przybyła, M. Shardlow (2020). Multi-Word Lexical Simplification. In: COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. pp.1435-1446.
S. Khalid, S. Ul Hassan, M. Shardlow, D. Dancey, R. Nawaz Author Name Disambiguation on Ambiguous Data of Chinese Authors using Machine Learning Approaches. 26/9/2019.
P. Przybyła, NTH. Nguyen, M. Shardlow, G. Kontonatsios, S. Ananiadou (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). 6/2016. pp.614-620.
M. Shardlow (2014). Out in the open: Finding and categorising errors in the lexical simplification pipeline. In: Proceedings of the 9th International Conference on Language Resources and Evaluation, LREC 2014. pp.1583-1590.
M. Shardlow (2013). A comparison of techniques to automatically identify complex words. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics. pp.103-109.
M. Shardlow (2013). The CW Corpus: A New Resource for Evaluating the Identification of Complex Words. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics. pp.69-77.