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 potential projects.
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.
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.
M. Shardlow, M. Ju, M. Li, C. O’Reilly, E. Iavarone, et al. (2018). A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience. Neuroinformatics.
I. Korkontzelos, A. Nikfarjam, M. Shardlow, A. Sarker, S. Ananiadou, et al. (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. Piotr, MJ. Shardlow, A. Sophie, B. Robert, EDC. Richard, et al. (2016). Text mining resources for the life sciences. Database : the Journal of Biological Databases and Curation.
M. Shardlow A Survey of Automated Text Simplification. International Journal of Advanced Computer Science and Applications. 4(1),
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.
S. Mohammad, MUS. Khan, M. Ali, L. Liu, M. Shardlow, et al. (2019). Bot detection using a single post on social media. In: 2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4). London, UK, 30/7/2019. pp.215-220.
M. Shardlow, R. Nawaz (2019). Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table. Florence, Italy, 29/7/2019.
L. Gerber, MJ. Shardlow (2018). Manchester Metropolitan at SemEval-2018 Task 2: Random Forest with an Ensemble of Features for Predicting Emoji in Tweets. In: Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018). New Orleans, USA, 5/6/2018. pp.491-496.
MJ. Shardlow, N. Nguyen, G. Owen, C. O'Donovan, A. Leach, et al. (2018). A New Corpus to Support Text Mining for the Curation of Metabolites in the ChEBI Database. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). Miyazaki, Japan, 7/5/2018. pp.280-285.
P. Piotr, N. Nhung, MJ. Shardlow, K. Georgios, A. Sophia (2016). NaCTeM at SemEval-2016 Task 1: Inferring sentence-level semantic similarity from an ensemble of complementary lexical and sentence-level features.
MJ. Shardlow (2014). Out in the Open: Finding and Categorising Errors in the Lexical Simplification Pipeline.
MJ. Shardlow (2013). The CW Corpus: A New Resource for Evaluating the Identification of Complex Words.
MJ. Shardlow (2013). A Comparison of Techniques to Automatically Identify Complex Words.