Computing and Information Systems - Research Publications
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ItemThe patient is more dead than alive: exploring the current state of the multi-document summarization of the biomedical literature(ASSOC COMPUTATIONAL LINGUISTICS-ACL, 2022)
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ItemWhat does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural text(ASSOC COMPUTATIONAL LINGUISTICS-ACL, 2022)
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ItemChEMU-Ref: A corpus for modeling anaphora resolution in the chemical domain(Association for Computational Linguistics, 2021-01-01)Chemical patents contain rich coreference and bridging links, which are the target of this research. Specially, we introduce a novel annotation scheme, based on which we create the ChEMU-Ref dataset from reaction description snippets in English-language chemical patents. We propose a neural approach to anaphora resolution, which we show to achieve strong results, especially when jointly trained over coreference and bridging links.
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ItemImproved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration(Association for Computational Linguistics, 2020)
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ItemUsing natural language processing and VetCompass to understand antimicrobial usage patterns in Australia(Wiley, 2019-08-01)Background Currently there is an incomplete understanding of antimicrobial usage patterns in veterinary clinics in Australia, but such knowledge is critical for the successful implementation and monitoring of antimicrobial stewardship programs. Methods VetCompass Australia collects medical records from 181 clinics in Australia (as of May 2018). These records contain detailed information from individual consultations regarding the medications dispensed. One unique aspect of VetCompass Australia is its focus on applying natural language processing (NLP) and machine learning techniques to analyse the records, similar to efforts conducted in other medical studies. Results The free text fields of 4,394,493 veterinary consultation records of dogs and cats between 2013 and 2018 were collated by VetCompass Australia and NLP techniques applied to enable the querying of the antimicrobial usage within these consultations. Conclusion The NLP algorithms developed matched antimicrobial in clinical records with 96.7% accuracy and an F1 Score of 0.85, as evaluated relative to expert annotations. This dataset can be readily queried to demonstrate the antimicrobial usage patterns of companion animal practices throughout Australia.
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ItemDetecting Chemical Reactions in Patents(Australasian Language Technology Association, 2019)Extracting chemical reactions from patents is a crucial task for chemists working on chemical exploration. In this paper we introduce the novel task of detecting the textual spans that describe or refer to chemical reactions within patents. We formulate this task as a paragraph-level sequence tagging problem, where the system is required to return a sequence of paragraphs that contain a description of a reaction. To address this new task, we construct an annotated dataset from an existing proprietary database of chemical reactions manually extracted from patents. We introduce several baseline methods for the task and evaluate them over our dataset. Through error analysis, we discuss what makes the task complex and challenging, and suggest possible directions for future research.