Unsupervised inference of implicit biomedical events using context triggers

Chung, Jin-Woo; Yang, Woosung; Park, Jong C.

doi:10.1186/s12859-020-3341-0

Cited by 3 publications

(7 citation statements)

References 37 publications

(75 reference statements)

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“…An event may involve arguments that are distant from each other or from the trigger, peculiarly frequent in long documents such as biomedical publications. Compared to RE, an event extent might be composed of many sentences, and it is hard to find all arguments of one event instance in a single sentence (arguments-scattering) [73]. Simply put, events do not necessarily constitute continuous spans of text, and their components are fragmented.…”

Section: B Challengesmentioning

confidence: 99%

“…Closed-domain [69]- [72], [85], [89], [100], [115], [125], [133], [134], [136]- [138], [142], [143], [145], [148], [159]- [161], [165]- [167], [169], [180]- [185], [201], [202], [216]- [220], [229]- [233], [241] [186] [42]- [44], [46], [47], [58], [60], [65], [73], [99], [114], [135], [139], [140], [149], [162], [163], [177]- [179], [187]- [191], [199], [203]- [205...…”

Section: Cross-lingualmentioning

confidence: 99%

“…Bui et al [177] Pattern learning \w structured mapping Pham et al [187] Hybrid (Pattern matching + ML) Liu et al [188] Hybrid (Pattern matching + ML) Venugopal et al [190] Hybrid (MLN + SVM) Björne and Salakoski [237] SVM Liu et al [189] Hybrid (graph-based patterns + ML) [205] Bi-GRU Li et al [99] Tree-LSTM Li et al [60] Multi-pooling CNN Zhu and Zheng [47] Hybrid (CNN + RNN) Trieu et al [44] SciBERT + custom layers Chung et al [73] Patterns \w context triggers Wang et al [135] EE as QA Huang et al [139] SciBERT + GNN Ramponi et al [140] EE as sequence labeling Yu et al [204] Bi-LSTM + Tree-LSTM Zhang et al [186] Hybrid (Bi-LSTM + GCN)…”

Section: Liu Et Al [239] Svm Pairwise Extraction and Extensionmentioning

confidence: 99%

“…Most research has merely focused on extracting events within the sentence scope, where a single sentence is assumed to cover complete pieces of information. Per contra, writers usually adopt several even non-continuous sentences to articulate individual or multiple events; this also applies to many BioNLP-ST datasets [73]. In this sense, much less attention has been paid to interactions discussed across a whole docu-…”

Section: ) Document-level Event Extractionmentioning

confidence: 99%

“…Alternatively, Gupta et al [286] used recurrent and recursive neural networks to classify the type of simple binary biomedical events within and across sentence boundaries, leveraging on inter-sentential dependency paths between entity pairs. Chung et al [73] noted that if an entity is mentioned in a sentence, it is possible to find expressions in its adjacent sentences implying the presence of that entity. They called these expressions "context triggers" (example in Table 8).…”

Section: S2mentioning

confidence: 99%

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A Survey on Event Extraction for Natural Language Understanding: Riding the Biomedical Literature Wave

Frisoni¹,

Moro

Carbonaro

2021

IEEE Access

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Motivation: The scientific literature embeds an enormous amount of relational knowledge, encompassing interactions between biomedical entities, like proteins, drugs, and symptoms. To cope with the everincreasing number of publications, researchers are experiencing a surge of interest in extracting valuable, structured, concise, and unambiguous information from plain texts. With the development of deep learning, the granularity of information extraction is evolving from entities and pairwise relations to events. Events can model complex interactions involving multiple participants having a specific semantic role, also handling nested and overlapping definitions. After being studied for years, automatic event extraction is on the road to significantly impact biology in a wide range of applications, from knowledge base enrichment to the formulation of new research hypotheses. Results: This paper provides a comprehensive and up-to-date survey on the link between event extraction and natural language understanding, focusing on the biomedical domain. First, we establish a flexible event definition, summarizing the terminological efforts conducted in various areas. Second, we present the event extraction task, the related challenges, and the available annotated corpora. Third, we deeply explore the most representative methods and present an analysis of the current state-of-the-art, accompanied by performance discussion. To help researchers navigate the avalanche of event extraction works, we provide a detailed taxonomy for classifying the contributions proposed by the community. Fourth, we compare solutions applied in biomedicine with those evaluated in other domains, identifying research opportunities and providing insights for strategies not yet explored. Finally, we discuss applications and our envisions about future perspectives, moving the needle on explainability and knowledge injection.INDEX TERMS Biomedical text mining, event extraction, natural language understanding, semantic parsing.

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Section: B Challengesmentioning

confidence: 99%

Section: Cross-lingualmentioning

confidence: 99%

Section: Liu Et Al [239] Svm Pairwise Extraction and Extensionmentioning

confidence: 99%

Section: ) Document-level Event Extractionmentioning

confidence: 99%

Section: S2mentioning

confidence: 99%

See 3 more Smart Citations

A Survey on Event Extraction for Natural Language Understanding: Riding the Biomedical Literature Wave

Frisoni¹,

Moro

Carbonaro

2021

IEEE Access

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Biomedical Natural Language Inference on Clinical trials using the BERT-based Models

Seerat,

Nasir,

Wasim

et al. 2024

Procedia Computer Science

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AT-NeuroEAE: A Joint Extraction Model of Events With Attributes for Research Sharing-Oriented Neuroimaging Provenance Construction

Lin

Sheng

et al. 2022

Front. Neurosci.

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Provenances are a research focus of neuroimaging resources sharing. An amount of work has been done to construct high-quality neuroimaging provenances in a standardized and convenient way. However, besides existing processed-based provenance extraction methods, open research sharing in computational neuroscience still needs one way to extract provenance information from rapidly growing published resources. This paper proposes a literature mining-based approach for research sharing-oriented neuroimaging provenance construction. A group of neuroimaging event-containing attributes are defined to model the whole process of neuroimaging researches, and a joint extraction model based on deep adversarial learning, called AT-NeuroEAE, is proposed to realize the event extraction in a few-shot learning scenario. Finally, a group of experiments were performed on the real data set from the journal PLOS ONE. Experimental results show that the proposed method provides a practical approach to quickly collect research information for neuroimaging provenance construction oriented to open research sharing.

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Unsupervised inference of implicit biomedical events using context triggers

Cited by 3 publications

References 37 publications

A Survey on Event Extraction for Natural Language Understanding: Riding the Biomedical Literature Wave

A Survey on Event Extraction for Natural Language Understanding: Riding the Biomedical Literature Wave

Biomedical Natural Language Inference on Clinical trials using the BERT-based Models

AT-NeuroEAE: A Joint Extraction Model of Events With Attributes for Research Sharing-Oriented Neuroimaging Provenance Construction

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