Professor Mark Lee

Dr Mark Lee

School of Computer Science
Professor of Artificial Intelligence

Contact details

Address
School of Computer Science
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Professor Mark Lee is a professor of artificial intelligence in the School of Computer Science. His research interests are focussed on Natural Language Processing. He is specifically interested in Sentiment Analysis of text, the automatic identification and understanding of metaphor and the effects of pragmatic inference in dialogue processing. More recently he has been investigating the extraction of constraints from text to build formal models for reasoning. He is the Principal Investigator of an EPSRC funded project on Automated Conflict Resolution in Clinical Pathways.

For more information, please see Mark's personal homepage.

Publications

Recent publications

Article

Litchfield, I, Turner, A, Ferreira Filho, JB, Lee, M & Weber, P 2022, 'Automated conflict resolution for patients with multiple morbidity being treated using more than one set of single condition clinical guidance: a case study', Computers in biology and medicine, vol. 144, 105381. https://doi.org/10.1016/j.compbiomed.2022.105381

Alharbi, AI, Smith, P & Lee, M 2022, 'Integrating character-level and word-level representation for affect in Arabic tweets', Data and Knowledge Engineering, vol. 138, 101973. https://doi.org/10.1016/j.datak.2021.101973

Devine, RT, Kovatchev, V, Grumley Traynor, I, Smith, P & Lee, M 2022, 'Machine learning and deep learning systems for automated measurement of ‘advanced’ theory of mind: reliability and validity in children and adolescents', Psychological Assessment.

Samsudin, NH & Lee, M 2021, 'An analysis of perceptual confusions on logatome utterances for similar language', Intelligent Automation and Soft Computing, vol. 32, no. 2, pp. 1025-1039. https://doi.org/10.32604/iasc.2022.022180

Chapter (peer-reviewed)

Kovatchev, V, Smith, P, Lee, M & Devine, RT 2021, Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children’s mindreading ability. in C Zong, F Xia, W Li & R Navigli (eds), Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). International Joint Conference on Natural Language Processing (IJCNLP), Association for Computational Linguistics, ACL, The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing , Bangkok, Thailand, 1/08/21. <https://arxiv.org/abs/2106.01635>

Conference article

Alharbi, AI, Smith, P & Lee, M 2021, 'Enhancing contextualised language models with static character and word embeddings for emotional intensity and sentiment strength detection in Arabic tweets', Procedia CIRP, vol. 189, pp. 258-265. https://doi.org/10.1016/j.procs.2021.05.089

Conference contribution

Kovatchev, V, Smith, P, Lee, M & Devine, R 2021, Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children's mindreading ability. in C Zong, F Xia, W Li & R Navigli (eds), ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference. vol. 1, Proceedings of the conference - Association for Computational Linguistics. Meeting, Association for Computational Linguistics, ACL, pp. 1196-1206, Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021, Virtual, Online, 1/08/21. https://doi.org/10.18653/v1/2021.acl-long.96

Gokhan, T, Smith, P & Lee, M 2021, Extractive financial narrative summarisation using SentenceBERT-based clustering. in Proceedings of the 3rd Financial Narrative Processing Workshop FNP 2021., 18, Proceedings of the conference - Association for Computational Linguistics. Meeting, Association for Computational Linguistics, ACL, pp. 94-98, 3rd Financial Narrative Processing Workshop, FNP 2021, Lancaster, United Kingdom, 15/09/21. <https://aclanthology.org/2021.fnp-1.18.pdf>

Laureano De Leon, FA, Tayyar Madabushi, H & Lee, M 2021, UoB at ProfNER 2021: data augmentation for classification using machine translation. in A Magge, A Klein, A Miranda-Escalada, MA Al-garadi, I Alimova, Z Miftahutdinov, E Farre-Maduell, S Lima-Lopez, I Flores, K O'Connor, D Weissenbacher, E Tutubalina, A Sarker, JM Banda, M Krallinger & G Gonzalez-Hernandez (eds), Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task. Social Media Mining for Health (SMM4H), Association for Computational Linguistics, ACL, pp. 115–117, Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, Mexico City, Mexico, 10/06/21. https://doi.org/10.18653/v1/2021.smm4h-1.23

Alnafesah, G, Tayyar Madabushi, H & Lee, M 2020, Augmenting Neural Metaphor Detection with Concreteness. in B Beigman Klebanov, E Shutova , P Lichtenstein, S Muresan, C Wee, A Feldman & D Ghosh (eds), Proceedings of the Second Workshop on Figurative Language Processing. Association for Computational Linguistics, ACL, pp. 204-210, Second Workshop on Figurative Language Processing (FigLang2020), Virtual event, 9/07/20. <https://www.aclweb.org/anthology/2020.figlang-1.28>

Alharbi, AI & Lee, M 2020, BhamNLP at SemEval-2020 Task 12: An Ensemble of Different Word Embeddings and Emotion Transfer Learning for Arabic Offensive Language Identification in Social Media. in A Herbelot, X Zhu, A Palmer, N Schneider, J May & E Shutova (eds), 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings. 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings, International Committee for Computational Linguistics, pp. 1532-1538, 14th International Workshops on Semantic Evaluation, SemEval 2020, Barcelona, Spain, 12/12/20.

Alharbi, AI & Lee, M 2020, Combining Character and Word Embeddings for the Detection of Offensive Language in Arabic. in H Al-Khalifa, W Magdy, K Darwish, T Elsayed & H Mubarak (eds), Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection: at LREC 2020 - Language Resources and Evaluation Conference. European Language Resources Association (ELRA), pp. 91-96, 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection (OSACT4 2020), Marseille, France, 11/05/20. <https://www.aclweb.org/anthology/2020.osact-1.15.pdf>

Alharbi, AI & Lee, M 2020, Combining character and word embeddings for affect in arabic informal social media microblogs. in E Métais, F Meziane, H Horacek & P Cimiano (eds), Natural Language Processing and Information Systems - 25th International Conference on Applications of Natural Language to Information Systems, NLDB 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12089 LNCS, Springer Vieweg, pp. 213-224, 25th International Conference on Applications of Natural Language to Information Systems, NLDB 2020, Saarbrücken, Germany, 24/06/20. https://doi.org/10.1007/978-3-030-51310-8_20

Hassan, FM & Lee, M 2020, Multi-stage News-Stance Classification Based on Lexical and Neural Features. in Á Herrero, C Cambra, D Urda, J Sedano, H Quintián & E Corchado (eds), 13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020)., Chapter 21, Advances in Intelligent Systems and Computing, vol. 1267, Springer, pp. 218-228, 13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020), Burgos, Spain, 16/09/20. https://doi.org/10.1007/978-3-030-57805-3_21

Preprint

Kovatchev, V, Smith, P, Lee, M & Devine, R 2021 'Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children's mindreading ability' arXiv. <https://arxiv.org/abs/2106.01635v1>

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