Search Results for author: Yulia Otmakhova

Found 7 papers, 0 papers with code

Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration

no code implementations EMNLP (NLP-COVID19) 2020 Yulia Otmakhova, Karin Verspoor, Timothy Baldwin, Simon Šuster

Efficient discovery and exploration of biomedical literature has grown in importance in the context of the COVID-19 pandemic, and topic-based methods such as latent Dirichlet allocation (LDA) are a useful tool for this purpose.

Topic Models

The patient is more dead than alive: exploring the current state of the multi-document summarisation of the biomedical literature

no code implementations ACL 2022 Yulia Otmakhova, Karin Verspoor, Timothy Baldwin, Jey Han Lau

Although multi-document summarisation (MDS) of the biomedical literature is a highly valuable task that has recently attracted substantial interest, evaluation of the quality of biomedical summaries lacks consistency and transparency.

ITTC @ TREC 2021 Clinical Trials Track

no code implementations16 Feb 2022 Thinh Hung Truong, Yulia Otmakhova, Rahmad Mahendra, Timothy Baldwin, Jey Han Lau, Trevor Cohn, Lawrence Cavedon, Damiano Spina, Karin Verspoor

This paper describes the submissions of the Natural Language Processing (NLP) team from the Australian Research Council Industrial Transformation Training Centre (ITTC) for Cognitive Computing in Medical Technologies to the TREC 2021 Clinical Trials Track.

Natural Language Processing

COVID-SEE: Scientific Evidence Explorer for COVID-19 Related Research

no code implementations18 Aug 2020 Karin Verspoor, Simon Šuster, Yulia Otmakhova, Shevon Mendis, Zenan Zhai, Biaoyan Fang, Jey Han Lau, Timothy Baldwin, Antonio Jimeno Yepes, David Martinez

We present COVID-SEE, a system for medical literature discovery based on the concept of information exploration, which builds on several distinct text analysis and natural language processing methods to structure and organise information in publications, and augments search by providing a visual overview supporting exploration of a collection to identify key articles of interest.

Natural Language Processing

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