A Search-based Neural Model for Biomedical Nested and Overlapping Event Detection

Abstract

We tackle the nested and overlapping event detection task and propose a novel search-based neural network (SBNN) structured prediction model that treats the task as a search problem on a relation graph of trigger-argument structures. Unlike existing structured prediction tasks such as dependency parsing, the task targets to detect DAG structures, which constitute events, from the relation graph. We define actions to construct events and use all the beams in a beam search to detect all event structures that may be overlapping and nested. The search process constructs events in a bottom-up manner while modelling the global properties for nested and overlapping structures simultaneously using neural networks. We show that the model achieves performance comparable to the state-of-the-art model Turku Event Extraction System (TEES) on the BioNLP Cancer Genetics (CG) Shared Task 2013 without the use of any syntactic and hand-engineered features. Further analyses on the development set show that our model is more computationally efficient while yielding higher F1-score performance.

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

“…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.…”

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

“…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.…”

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

“…Such approaches ignore the close interaction between named entities and their relation information and typically suffer from the error propagation problem. To overcome these limitations, many studies have proposed joint models that perform entity extraction and relation extraction simultaneously (Roth and Yih, 2007;Li and Ji, 2014;Li et al, 2017;Zheng et al, 2017;Bekoulis et al, 2018a,b;Zhao et al, 2020;Wang and Lu, 2020;Li et al, 2020b;Lin et al, 2020) 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.…”