Papers by Nhung Nguyen
A New Corpus to Support Text Mining for the Curation of Metabolites in the ChEBI Database (L18-1)
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Matthew Shardlow, Nhung Nguyen, Gareth Owen, Claire O’Donovan, Andrew Leach, John McNaught, Steve Turner, Sophia Ananiadou
| Challenge: | a corpus of 200 abstracts and 100 full text papers which have been annotated with named entities and relations in the biomedical domain is part of the OpenMinTeD project. |
| Approach: | They propose to annotate 200 abstracts and 100 full text papers with entities and relations in the biomedical domain as part of the OpenMinTeD project. |
| Outcome: | The proposed corpus can be used within ChEBI to facilitate text and data mining and integrate with the OpenMinTeD text and database platform. |
A Named Entity Recognition Corpus for Vietnamese Biomedical Texts to Support Tuberculosis Treatment (2022.lrec-1)
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| Challenge: | Named Entity Recognition (NER) is an important task in information extraction. |
| Approach: | They construct a labelled NER corpus of Vietnamese academic biomedical text . they annotate documents with five categories of named entities: Organisation, Location, Date and Time, Symptom and Disease, and Diagnostic Procedure. |
| Outcome: | The proposed system could provide answers to questions related to TB in Vietnamese . the system could also be used to identify TB-related diseases in the country . |
An Empirical Investigation of Error Types in Vietnamese Parsing (C18-1)
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| Challenge: | Syntactic parsing improves the quality of natural language processing tasks. |
| Approach: | They evaluated Vietnamese Treebank model to find most suitable parsing method . they found that Vietnamese parsers produced limited training data and POS errors . |
| Outcome: | The proposed method improves the parsing quality in Vietnamese . the results highlight three possible sources of parser errors . |
Coreference Resolution in Full Text Articles with BERT and Syntax-based Mention Filtering (D19-57)
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| Challenge: | Existing systems for coreference resolution are difficult because of their long coreferent chains. |
| Approach: | They propose to use an existing span-based neural coreference resolution system as a baseline . they filter noisy mentions based on parse trees and integrate a highly expressive language model into the system . |
| Outcome: | The proposed system outperforms the baseline system on the CRAFT Shared Tasks 2019 task. |