Nhung T. H. Nguyen


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Span-based Named Entity Recognition by Generating and Compressing Information
Nhung T. H. Nguyen | Makoto Miwa | Sophia Ananiadou
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

The information bottleneck (IB) principle has been proven effective in various NLP applications. The existing work, however, only used either generative or information compression models to improve the performance of the target task. In this paper, we propose to combine the two types of IB models into one system to enhance Named Entity Recognition (NER).For one type of IB model, we incorporate two unsupervised generative components, span reconstruction and synonym generation, into a span-based NER system. The span reconstruction ensures that the contextualised span representation keeps the span information, while the synonym generation makes synonyms have similar representations even in different contexts. For the other type of IB model, we add a supervised IB layer that performs information compression into the system to preserve useful features for NER in the resulting span representations. Experiments on five different corpora indicate that jointly training both generative and information compression models can enhance the performance of the baseline span-based NER system. Our source code is publicly available at https://github.com/nguyennth/joint-ib-models.


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Investigating Domain-Specific Information for Neural Coreference Resolution on Biomedical Texts
Hai-Long Trieu | Nhung T. H. Nguyen | Makoto Miwa | Sophia Ananiadou
Proceedings of the BioNLP 2018 workshop

Existing biomedical coreference resolution systems depend on features and/or rules based on syntactic parsers. In this paper, we investigate the utility of the state-of-the-art general domain neural coreference resolution system on biomedical texts. The system is an end-to-end system without depending on any syntactic parsers. We also investigate the domain specific features to enhance the system for biomedical texts. Experimental results on the BioNLP Protein Coreference dataset and the CRAFT corpus show that, with no parser information, the adapted system compared favorably with the systems that depend on parser information on these datasets, achieving 51.23% on the BioNLP dataset and 36.33% on the CRAFT corpus in F1 score. In-domain embeddings and domain-specific features helped improve the performance on the BioNLP dataset, but they did not on the CRAFT corpus.


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NaCTeM at SemEval-2016 Task 1: Inferring sentence-level semantic similarity from an ensemble of complementary lexical and sentence-level features
Piotr Przybyła | Nhung T. H. Nguyen | Matthew Shardlow | Georgios Kontonatsios | Sophia Ananiadou
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


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A Rule-Augmented Statistical Phrase-based Translation System
Cong Duy Vu Hoang | AiTi Aw | Nhung T. H. Nguyen
Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations


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Extracting Bacteria Biotopes with Semi-supervised Named Entity Recognition and Coreference Resolution
Nhung T. H. Nguyen | Yoshimasa Tsuruoka
Proceedings of BioNLP Shared Task 2011 Workshop