The Women in AI speaker series, a collaboration between the Schwartz Reisman Institute for Technology and Society and Deloitte, welcomes Sophia Ananiadou, a professor of computer science at the University of Manchester, and director of UK National Centre for Text Mining, which provides tools, resources, systems and infrastructure for biomedicine.
Ananiadou’s research focuses on natural language processing (NLP) and text mining for biomedical contexts. In this session, she will explore how NLP techniques can be used to summarize research to increase the speed and reliability of knowledge discovery, and will discuss current trends in biomedical text summarization, the use of pre-trained language models (PLMs), benchmarks, evaluation measures, and challenges faced in both extractive and abstractive methods.
Talk title:
“Natural language processing for biomedicine”
Abstract:
Making sense of the growing literature across a wide range of research domains requires methods that will increase the speed and reliability of knowledge discovery. Moreover, due to the proliferation of scientific databases and ontologies, discovery of previously unknown knowledge demands that scientists engage with many resources, covering different levels and views of (multiple) domain spaces in context (e.g., degree of confidence in a finding). With the availability of large pre-trained transformer language models, neural natural language processing (NLP) models have been deployed for several downstream tasks, including information extraction and summarization. Information extraction (e.g., named entity recognition and inter-sentence relation extraction) can capture implicit relations. Moreover, events which encapsulate n-ary relationships (e.g., interactions between any number of concepts) are extracted with end-to-end neural methods, including capturing richer contextual information such as certainty and polarity.
Text summarization techniques are used to support users in accessing information efficiently, by retaining only the most important semantic information contained within documents. Text summarization is important in a variety of scenarios, including systematic reviews (synthesis), and evidence-based medicine. In this talk, I will discuss current trends in biomedical text summarization, the use of pre-trained language models (PLMs), benchmarks, evaluation measures, and challenges faced in both extractive and abstractive methods, including recent approaches such as hybrid unsupervised summarization methods using salience, the incorporation of fine-grained medical knowledge into PLMs to extractive summarization, and long document summarization using local and global semantics.
About Sophia Ananiadou
Sophia Ananiadou is a professor of computer science at The University of Manchester. Her main areas of research are natural language processing and text mining applied in biomedicine. Ananiadou is the director of the UK National Centre for Text Mining, deputy director of the Institute of Data Science and AI (Manchester), a Turing Fellow, an ELLIS member, and a distinguished research fellow at the AI Research Centre (AIST Japan). Following a Bachelors in Linguistics from the University of Athens, Ananiadou obtained two Masters degrees from Paris universities (Linguistics, Jussieu; Literature, Sorbonne), and a PhD in natural language processing from the University of Manchester’s Institute of Science and Technology. Her research interests evolved on how AI systems could acquire and exploit knowledge of language, particularly in specialised domains. She became involved in interdisciplinary research and played a leading role in bridging text mining to systems biology, bioinformatics, public health but also humanities and law, deepening her research into automatic term recognition, the link between linguistic terms and their surface variants, and ontologies, the design and construction of large-scale linguistic resources (annotated text corpora, computational lexicons), information extraction, semantic search, emotion detection, text summarization, and simplification. Ananiadou is co-instigator of a Special Interest Group within ACL (SIGBioMed) dedicated to language processing in the biological, biomedical, and clinical domain bringing together researchers in NLP, bioinformatics, medical informatics, and computational biology.
About Women in AI
Women in AI is a six-part virtual speaker and mentorship series developed by the Schwartz Reisman Institute for Technology and Society in collaboration with Deloitte that connects a global audience with a diverse group of female thought leaders in the field of AI research. The Women in AI series convenes leading female AI researchers to share knowledge and mentorship opportunities through seminar events that promote opportunities for women across the technology sector. Participants will explore how women are leading the development of new technologies and approaches, and investigating emerging trends across the sector. The series provides insights into cutting-edge research, bold points of views, and help business and community leaders elevate diverse voices while promoting opportunities for women to share their perspectives.
Sophia Ananiadou