Overview of BioNLP Shared Task 2013

By Jin-Dong Kim, Sampo Pyysalo, Tomoko Ohta, Robert Bossy, N. Nguyen, Junichi Tsujii. BioNLP@ACL, 2013

346 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4efa5ee94e64ff63b8ecd8d6809742f813844cd8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xu Han
2. Zhiyuan Liu
3. Yankai Lin
4. Arindam Mitra
5. Peng Li
6. Jie Zhou
7. Xiaozhi Wang
8. Dat Quoc Nguyen

Citations in the corpus

• From POS tagging to dependency parsing for biomedical event extraction (2018, methodology): “Therefore, we employ the online evaluation system for the BioNLP 2011 shared task [49] with the “abstracts only” option.”
• Learning to Automatically Solve Logic Grid Puzzles (2015, background): “Understanding natural language to solve problems be it algebraic word problems (Kushman et al., 2014; Hosseini et al., 2014) or questions from biology texts (Berant et al., 2014; Kim et al., 2011), has attracted a lot of research interest over the past few decades.”
• MAVEN: A Massive General Domain Event Detection Dataset (2020, background): “Some datasets are developed for ED on specific domains, like the biomedical domain (Pyysalo et al., 2007; Kim et al., 2008; Thompson et al., 2009; Buyko et al., 2010; Nédellec et al., 2013), literature (Sims et al.”
• Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models (2017, background): “So as to gain attraction among researchers many challenges such as BioNLP’09 (Kim et al., 2009), BioNLP’11 (Kim et al., 2011), BioNLP’13 (Nédellec et al., 2013) have been organized and many novel methods have also been proposed addressing these tasks.”
• Using word embedding for bio-event extraction (2015, background): “BioNLP Shared Tasks (BioNLP-ST) [7] have been consistently conducted since 2009 and attracted community-wide support.”
• Memory, Show the Way: Memory Based Few Shot Word Representation Learning (2018, methodology): “Domain Specific NER We use BioNLP11species (Kim et al., 2011), AnatEMs (Pyysalo and Ananiadou, 2013) and NCBI-disease (Doğan et al., 2014) dataset, respectively from taxonomy, anatomy and pathology literatures.”
• Bridging semantics and syntax with graph algorithms —state-of-the-art of extracting biomedical relations (2015, background): “BioNLP-ST-2013 further expanded the application domains with tasks of GE, BB, cancer genetics (CG), pathway curation (PC), and gene regulation ontology (GRO) [60].”
• Deep Learning with Minimal Training Data: TurkuNLP Entry in the BioNLP Shared Task 2016 (2016, methodology): “The 2013 BB task de-fined three subtasks (N´edellec et al., 2013), the first one concerning NER, targeting bacteria habitat entities and their normalization, and the other two subtasks involving relation extraction, the task targeted also by the system presented here.”
• Named Entity Recognition in the Medical Domain with Constrained CRF Models (2017, background): “With respect to our overall objective of entity and relation extraction from the medical literature, a large proportion of the related work originates from BioNLP event extraction (Kim et al., 2009; Nédellec et al., 2013; Chaix et al., 2016; Deléger et al., 2016).”