Coreference resolution in biomedical texts

Li, Lishuang; Jin, Liuke; Jiang, Zhenchao; Zhang, Jing; Huang, Degen

doi:10.1109/bibm.2014.6999392

Cited by 8 publications

(9 citation statements)

References 5 publications

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“…Protein and gene entities identified by BANNER, and noun phrases containing such entities are preferentially considered as antecedents for the definite noun phrases containing domain-specific terms such as ‘gene’, ‘protein’, ‘receptor’ and ‘molecule’. Even though performance of the simple coreference resolution system could not reach state-of-the-art systems such as Miwa et al ( 34 ), D'Souza and Ng ( 36 ) and Li et al ( 35 ), it outperforms the best published results for the BioNLP’11 Protein Coreference shared task, as shown in Table 3 . We use our simple coreference system, since those systems are not publicly available.…”

Section: Methodsmentioning

confidence: 92%

“…The incorporation of the coreference resolution slightly improved event extraction performance. A hybrid approach combining rule-based and machine learning-based methods has been employed for biomedical coreference resolution ( 35 , 36 ). D’Souza and Ng ( 36 ) used the combined approach for both mention detection and anaphora resolution.…”

Section: Introductionmentioning

confidence: 99%

“…D’Souza and Ng ( 36 ) used the combined approach for both mention detection and anaphora resolution. Li et al ( 35 ) also used the combined approach for some types of anaphoric mentions; they use both rule-based and machine learning methods for relative pronoun resolution, while exclusively rule-based approaches are applied for resolution of non-relative pronouns and definite noun phrases. Those recent works show that the use of different approaches in terms of anaphora types achieved substantial improvement comparing to the best performing system in the BioNLP task 2011.…”

Section: Introductionmentioning

confidence: 99%

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Coreference resolution improves extraction of Biological Expression Language statements from texts

et al. 2016

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We describe a system that automatically extracts biological events from biomedical journal articles, and translates those events into Biological Expression Language (BEL) statements. The system incorporates existing text mining components for coreference resolution, biological event extraction and a previously formally untested strategy for BEL statement generation. Although addressing the BEL track (Track 4) at BioCreative V (2015), we also investigate how incorporating coreference resolution might impact event extraction in the biomedical domain. In this paper, we report that our system achieved the best performance of 20.2 and 35.2 in F-score for the full BEL statement level on both stage 1, and stage 2 using provided gold standard entities, respectively. We also report that our results evaluated on the training dataset show benefit from integrating coreference resolution with event extraction.

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Section: Methodsmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Coreference resolution improves extraction of Biological Expression Language statements from texts

et al. 2016

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“…Typical works of Bio-entity coreference resolution use rule-based [4,5,6] and hybrid methods [7,8], which rely on syntactic features and are limited to a specific corpus. Recently, neural network-based methods for automatically identifying coreferences have received widespread attention.…”

Section: Context and Motivationmentioning

confidence: 99%

Context-Aware Feature Attention Model for Coreference Resolution on Biomedical Texts

Zhou

et al. 2021

Preprint

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Background: Bio-entity Coreference resolution is an important task to gain a complete understanding of biomedical texts automatically. Previous neural network-based studies on this topic are domain system based methods which rely on some domain-specific information integration. However, for the identical mentions, this may lead to misleading information, as the model tends to get similar or even identical representations, which further leads to wrongful predictions. Results: we propose a new context-aware Feature Attention model to distinguish identical mentions effectively to better resolve coreference. The new model can represent identical mentions based on different contexts by adaptively exploiting features effectively. The proposed model substantially outperforms the state-of-the-art baselines on the BioNLP dataset with a 64.0% F1 score and further demonstrates superior performance on the differential representation and coreferential link of identical mentions. Conclusion: The context-aware Feature Attention model adaptively exploit features and represent identical mentions according to different contexts, which significantly makes the system obtain semantic information effectively and make more accurate predictions. Considering that this approach is still limited when context information is insufficient, we expect to utilize such information more fine-grained with the help of the external knowledge base in coreference resolution.

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“…In the biomedical domain, coreference information has been shown to enhance the performance of entity and event extraction Choi et al, 2016a). Most of work in this domain use rule-based or hybrid approaches (Nguyen et al, 2011(Nguyen et al, , 2012D'Souza and Ng, 2012;Li et al, 2014;Choi et al, 2016b;Cohen et al, 2017). These systems rely on syntactic parsers to extract hand-crafted features and rules, e.g., rules based on predicate argument structure (Nguyen et al, 2012; or features based on syntax trees (D'Souza and Ng, 2012).…”

Section: Introductionmentioning

confidence: 99%

Investigating Domain-Specific Information for Neural Coreference Resolution on Biomedical Texts

Trieu¹,

Nguyen²,

Misawa³

et al. 2018

Proceedings of the BioNLP 2018 Workshop

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Existing biomedical coreference resolution systems depend on features and/or rules based on syntactic parsers. In this paper, we investigate the utility of the stateof-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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Coreference resolution in biomedical texts

Cited by 8 publications

References 5 publications

Coreference resolution improves extraction of Biological Expression Language statements from texts

Coreference resolution improves extraction of Biological Expression Language statements from texts

Context-Aware Feature Attention Model for Coreference Resolution on Biomedical Texts

Investigating Domain-Specific Information for Neural Coreference Resolution on Biomedical Texts

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