GENIA corpus - a semantically annotated corpus for bio-textmining

By Jin-Dong Kim, Tomoko Ohta, Yuka Tateisi, Junichi Tsujii. 2003

1,388 citations recorded by the source corpus.

Subfields: Molecular Biology, Natural Language Processing, Evaluation & Benchmarks

How researchers see this paper

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

In Molecular Biology

In Natural Language Processing

In Evaluation & Benchmarks

Citations in the corpus

SciBERT: A Pretrained Language Model for Scientific Text (2019, background): “t al., 2016) Bio 15,030 JNLPBA (Collier and Kim, 2004) Bio 24,806 NCBI-disease (Dogan et al., 2014) Bio 7,287 SciERC (Luan et al., 2018) CS 3,187 PICO EBM-NLP (Nye et al., 2018) Bio 50,362 DEP GENIA (Kim et al., 2003) Bio 17,047 CLS RCT-20k (Dernoncourt and Lee, 2017) Bio 240,387 ACL-ARC (Jurgens et”
Findings of the 2014 Workshop on Statistical Machine Translation (2014, background): “GENIA is a corpus of biomedical literature compiled and annotated within the GENIA project (Kim et al., 2003).”
Text-mining and information-retrieval services for molecular biology (2005, background): “This is partly because of newly available resources, such as collections of annotated documents suitable for training new systems (for example, the GENIA [77] corpus and the BioCreative [19] corpus).”
A Survey on Semantic Parsing (2018, methodology): “They evaluate their model on a fact prediction task on a KB that they extract from the GENIA [Kim et al., 2003] biomedical corpus.”
Corpus annotation for mining biomedical events from literature (2008, background): “For the details of the term ontology, please refer to [32].”
Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports (2012, methodology): “In the biological domain, efforts have been made to generate semantically annotated corpora like GENIA [10], BioCreative9 and BioNLP.10 However, these bio-corpora are restricted to the entities and events of biological interest such as gene names, protein names, cellular location or cellular events ”
A Unified MRC Framework for Named Entity Recognition (2019, background): “The overlapping between entities (mentions) was ﬁrst noticed by Kim et al. (2003), who developed handcrafted rules to identify overlapping mentions.”
Concept annotation in the CRAFT corpus (2012, methodology): “…corpora, as these are obviously most directly related: ABGene [42], BioInfer [43], the CLEF Corpus [44], the FetchProt Corpus [45], the Fourth i2b2/VA Challenge corpus [46], GENETAG [47], GENIA [48,49], GREC [50], the ITI TXM PPI and TE corpora [51], MedPost [52], the PennBioIE Oncology and CYP v1.”
Machine Reading: A "Killer App" for Statistical Relational AI (2010, methodology): “Experiments We applied OntoUSP to extract knowledge from the GENIA dataset (Kim et al. 2003) and answer questions, and we evaluated it on the number of extracted answers and accuracy.”
Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains (2019, background): “It is a corpus annotated over 24 open access full-text articles from the GENIA corpus (Kim et al., 2003) in the biomedical domain.”
Unsupervised Ontology Induction from Text (2010, methodology): “Speciﬁcally, we applied OntoUSP to extract knowledge from the GE-NIA dataset (Kim et al., 2003) and answer questions, and we evaluated it on the number of extracted answers and accuracy.”
OAG-BERT: Pre-train Heterogeneous Entity-augmented Academic Language Models (2021, methodology): “The DEP task [20] also uses the token outputs from the pre-training model.”
An extensive review of tools for manual annotation of documents (2019, background): “Eight of the 13 tools that we evaluated in our previous survey [11] failed to comply with the Web application requirement and were removed from our new evaluation: @Note, Callisto, GATE, Knowtator, MMAX2, Semantator, WordFreak and XConc Suite.”
Mining the Biomedical Literature in the Genomic Era: An Overview (2003, neutral): “The GENIA corpus (Kim et al. , 2003) is a collection currently being built to support training and testing of NLP methods for biomedical text mining.”