35 citations · 48 across the 5 of their papers we have counts for
5 papers
CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification
Matúš Falis, Hang Dong, Alexandra Birch +1
Large-Scale Multi-Label Text Classification (LMTC) includes tasks with hierarchical label spaces, such as automatic assignment of ICD-9 codes to discharge summaries. Performance of…
The Online Pivot: Lessons Learned from Teaching a Text and Data Mining Course in Lockdown, Enhancing online Teaching with Pair Programming and Digital Badges
Beatrice Alex, Clare Llewellyn, Pawel Michal Orzechowski +1
In this paper we provide an account of how we ported a text and data mining course online in summer 2020 as a result of the COVID-19 pandemic and how we improved it in a second pil…
Situated Data, Situated Systems: A Methodology to Engage with Power Relations in Natural Language Processing Research
Lucy Havens, Melissa Terras, Benjamin Bach +1
We propose a bias-aware methodology to engage with power relations in natural language processing (NLP) research. NLP research rarely engages with bias in social contexts, limiting…
Plague Dot Text: Text mining and annotation of outbreak reports of the Third Plague Pandemic (1894-1952)
Arlene Casey, Mike Bennett, Richard Tobin +4
The design of models that govern diseases in population is commonly built on information and data gathered from past outbreaks. However, epidemic outbreaks are never captured in st…
Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches
Philip John Gorinski, Honghan Wu, Claire Grover +6
This work investigates multiple approaches to Named Entity Recognition (NER) for text in Electronic Health Record (EHR) data. In particular, we look into the application of (i) rul…