SemEval-2013 Task 9 : Extraction of Drug-Drug Interactions from Biomedical Texts (DDIExtraction 2013)

By Isabel Segura-Bedmar, Paloma Martínez, María Herrero-Zazo. International Workshop on Semantic Evaluation, 2013

478 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a9a5b40e179ed22e3d59b7823bffc9d3aaa65474

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Citations in the corpus

• Constructing Datasets for Multi-hop Reading Comprehension Across Documents (2017, background): “Existing DDI efforts have focused on explicit mentions of interactions in single sentences (Gurulingappa et al., 2012; Percha et al., 2012; Segura-Bedmar et al., 2013).”
• Bridging semantics and syntax with graph algorithms - state-of-the-art of extracting biomedical relations (2017, methodology): “5 relation corpus [133], DDIExtraction relation corpora [55, 56], i2b2/VA corpus [1], AIMed [136], BioInfer [124], HPRD50 [137], IEPA”
• A neural joint model for entity and relation extraction from biomedical text (2017, background): “Recently, various related tasks have been proposed, such as protein-protein interaction detection (PPI) [2], drug-drug interaction detection (DDI) [3], adverse drug event extraction (ADE) [4] and the bacteria biotope task (BB) [5].”
• Extracting Drug-Drug Interactions with Attention CNNs (2017, methodology): “We evaluate our model on the Task 9.2 of the DDIExtraction-2013 shared task (Segura Bedmar et al., 2013).”
• Bridging semantics and syntax with graph algorithms —state-of-the-art of extracting biomedical relations (2015, background): “The state-of-the-art relation and event extraction systems from shared tasks are all built around domain-specific definitions of relations and events, many of which are in fact binary (e.g., Bi-oCreative PPI challenge [98], DDIExtraction challenge [50,51], and i2b2/VA challenge [1]).”
• Graph convolutional networks for computational drug development and discovery (2019, methodology): “The drug structure was obtained from DrugBank and text documents were from DDIExtraction 2013 shared task [70].”
• Drug drug interaction extraction from biomedical literature using syntax convolutional neural network (2016, background): “In recent years, DDIExtraction 2011 (Segura Bedmar et al. , 2011) and 2013 challenges (Segura Bedmar et al. , 2013) have been held successfully.”
• A Survey on Recent Advances in Named Entity Recognition from Deep Learning models (2018, methodology): “In DrugNER (Segura Bedmar et al., 2013), Liu et al. (2015) achieved state-of-the-art results by us-ing a CRF with features like lexicon resources from Food and Drug Administration (FDA), DrugBank, Jochem (Hettne et al., 2009) and word embeddings (trained on a MedLine corpus).”
• A Comparison of Word Embeddings for the Biomedical Natural Language Processing (2018, methodology): “In this experiment, we evaluate word embeddings on DDIExtraction 2013 challenge corpus [58].”
• Effects of Semantic Features on Machine Learning-Based Drug Name Recognition Systems: Word Embeddings vs. Manually Constructed Dictionaries (2015, background): “There has been a lot of research on DNR, including the drug-drug interaction extraction (DDIExtraction) 2013 challenge [2].”
• DARE: Data Augmented Relation Extraction with GPT-2 (2020, background): “The DDIExtraction 2013 corpus (Segura Bedmar et al., 2013) contains MedLine abstracts and DrugBank documents describing drug-drug interactions.”