Biomedical Event Extraction as Sequence Labeling

Ramponi, Alan; Goot, Rob van der; Lombardo, Rosario; Plank, Barbara

doi:10.18653/v1/2020.emnlp-main.431

Cited by 34 publications

(37 citation statements)

References 25 publications

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“…Data Similarly to the recent work (Li et al, 2019;Huang et al, 2020;Ramponi et al, 2020), we also conduct experiments on the BioNLP GENIA 2011 (Kim et al, 2011) is annotated in paragraphs. Following (Li et al, 2019), we focus on sentence-level event extraction and only keep events and argument roles within each sentence (around 94% of the events).…”

Section: Methodsmentioning

confidence: 99%

“…We consider the most recent models on biomedical event extraction: KB-Tree-LSTM (Li et al, 2019), GEANet (Huang et al, 2020), BEESL (Ramponi et al, 2020), and DeepEventMine (Trieu et al, 2020) for comparison in our experiments, and we report the precision, recall, and F1 score from the GENIA 2011 online test set evaluation service 7 .…”

Section: Baselines and Ablation Variantsmentioning

confidence: 99%

“…We report the performance of our model and compare it with the most recent biomedical IE models KB-Tree-LSTM (Li et al, 2019), GEANet (Huang et al, 2020), BEESL (Ramponi et al, 2020), and DeepEventMine (Trieu et al, 2020) in Table 3. In general, our KG enriched AMR model can achieve slightly higher performance compared with the state-of-the-art model DeepEventMine.…”

Section: Overall Performancementioning

confidence: 99%

“…Biomedical Information Extraction A number of previous studies contribute to biomedical event extraction with various techniques, such as dependency parsing (McClosky et al, 2011;Li et al, 2019), external knowledge base (Li et al, 2019;Huang et al, 2020), joint inference of triggers and arguments (Poon and Vanderwende, 2010;Ramponi et al, 2020), Abstract Meaning Representation (Rao et al, 2017), search based neural models (Espinosa et al, 2019), and multi-turn question answering (Wang et al, 2020b). Recently, to handle the nested biomedical events, BEESL (Ramponi et al, 2020) models biomedical event extraction as a unified sequence labeling problem for end-to-end training.…”

Section: Related Workmentioning

confidence: 99%

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Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation

Zhang¹,

Parulian²,

Ji³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Biomedical Information Extraction from scientific literature presents two unique and nontrivial challenges. First, compared with general natural language texts, sentences from scientific papers usually possess wider contexts between knowledge elements. Moreover, comprehending the fine-grained scientific entities and events urgently requires domain-specific background knowledge. In this paper, we propose a novel biomedical Information Extraction (IE) model to tackle these two challenges and extract scientific entities and events from English research papers. We perform Abstract Meaning Representation (AMR) to compress the wide context to uncover a clear semantic structure for each complex sentence. Besides, we construct the sentence-level knowledge graph from an external knowledge base and use it to enrich the AMR graph to improve the model's understanding of complex scientific concepts. We use an edge-conditioned graph attention network to encode the knowledgeenriched AMR graph for biomedical IE tasks. Experiments on the GENIA 2011 dataset show that the AMR and external knowledge have contributed 1.8% and 3.0% absolute F-score gains respectively. In order to evaluate the impact of our approach on real-world problems that involve topic-specific fine-grained knowledge elements, we have also created a new ontology and annotated corpus for entity and event extraction for the COVID-19 scientific literature, which can serve as a new benchmark for the biomedical IE community. 1

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Section: Methodsmentioning

confidence: 99%

Section: Baselines and Ablation Variantsmentioning

confidence: 99%

Section: Overall Performancementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation

Zhang¹,

Parulian²,

Ji³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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“…To the best of our knowledge, our work is the first span-based frame-work that utilizes external knowledge for joint entity and relation extraction from biomedical text. Biomedical event extraction is a closely related task that has also received a lot of attention from the research community (Poon and Vanderwende, 2010;Kim et al, 2013;V S S Patchigolla et al, 2017;Rao et al, 2017;Espinosa et al, 2019;Ramponi et al, 2020;Yadav et al, 2020). Several studies have proposed to incorporate external knowledge from domain-specific KBs into neural models for biomedical event extraction.…”

Section: Related Workmentioning

confidence: 99%

Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference

Lai¹,

Ji²,

Zhai³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Compared to the general news domain, information extraction (IE) from biomedical text requires much broader domain knowledge. However, many previous IE methods do not utilize any external knowledge during inference. Due to the exponential growth of biomedical publications, models that do not go beyond their fixed set of parameters will likely fall behind. Inspired by how humans look up relevant information to comprehend a scientific text, we present a novel framework that utilizes external knowledge for joint entity and relation extraction named KECI (Knowledge-Enhanced Collective Inference). Given an input text, KECI first constructs an initial span graph representing its initial understanding of the text. It then uses an entity linker to form a knowledge graph containing relevant background knowledge for the the entity mentions in the text. To make the final predictions, KECI fuses the initial span graph and the knowledge graph into a more refined graph using an attention mechanism. KECI takes a collective approach to link mention spans to entities by integrating global relational information into local representations using graph convolutional networks. Our experimental results show that the framework is highly effective, achieving new state-of-theart results in two different benchmark datasets: BioRelEx (binding interaction detection) and ADE (adverse drug event extraction). For example, KECI achieves absolute improvements of 4.59% and 4.91% in F1 scores over the stateof-the-art on the BioRelEx entity and relation extraction tasks 1 .

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Decision Support System for Online Recruitment

Ramdani

Monticolo

Brun

et al. 2021

Lecture Notes in Business Information Processing

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Biomedical Event Extraction as Sequence Labeling

Cited by 34 publications

References 25 publications

Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation

Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation

Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference

Decision Support System for Online Recruitment

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