The GENIA corpus: an annotated research abstract corpus in molecular biology domain

By Tomoko Ohta, Yuka Tateisi, Jin-Dong Kim. 2002

339 citations recorded by the source corpus.

With the information overload in genome-related field, there is an increasing need for natural language processing technology to extract information from literature and various attempts of information extraction using NLP has been being made. We are developing the necessary resources including domain ontology and annotated corpus from research abstracts in MEDLINE database (GENIA corpus). We are building the ontology and the corpus simultaneously, using each other. In this paper we report on our new corpus, its ontological basis, annotation scheme, and statistics of annotated objects. We also describe the tools used for corpus annotation and management.

Subfields: Artificial Intelligence, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

GLM-130B: An Open Bilingual Pre-trained Model (2022, methodology): “We design two different types of tasks based on named entity recognition datasets CoNLL03 (Sang & Meulder, 2003), OntoNotes 5.0 (Pradhan et al., 2013), and GENIA (Ohta et al., 2002).”
Semi-supervised Multitask Learning for Sequence Labeling (2017, methodology): “In addition, we use the POS-annotated subset of the GENIA corpus (Ohta et al., 2002) containing 2,000 biomedical PubMed abstracts.”
BioInfer: a corpus for information extraction in the biomedical domain (2007, methodology): “While most of the twenty corpora provide named entity annotation of some kind, the de facto standard for biomedical named entity recognition research is the Genia corpus [ 8 ].”
Nested Named Entity Recognition (2009, methodology): “In the GENIA corpus (Ohta et al., 2002), which is labeled with entity types such asprotein andDNA, roughly 17% of entities are embedded within another entity.”
Multi-Task Active Learning for Linguistic Annotations (2008, methodology): “is based on the parsed section of the GENIA corpus (Ohta et al., 2002).”
Structured Prediction as Translation between Augmented Natural Languages (2021, methodology): “We experiment on two ﬂat NER datasets, CoNLL03 (Sang & Meulder, 2003) and OntoNotes (Pradhan et al., 2013), and two nested NER datasets, GENIA (Ohta et al., 2002) and ACE2005 (Walker et al., 2006).”
Corpus annotation for mining biomedical events from literature (2008, methodology): “In earlier work, our laboratory constructed such an annotated corpus, the GENIA corpus, and made it publicly available to the community [31-33].”
A Survey on Recent Advances in Named Entity Recognition from Deep Learning models (2018, background): “Ohta et al. (2002) constructed a dataset of nested named entities, where one named entity can contain another.”
Term identification in the biomedical literature (2004, background): “4 The GENIA corpus is a manually annotated collection of 2,000 biomedical abstracts [8], in which term”
Supervised Noun Phrase Coreference Research: The First Fifteen Years (2010, background): “, 2007b), which consists of 287 Japanese news articles; (5) the AnCora Corpus (Recasens and Martı́, 2009), which consists of Spanish and Catalan journalist texts; and (6) the GENIA corpus (Ohta et al., 2002), which contains 2000 MEDLINE abstracts.”
GENIA corpus - a semantically annotated corpus for bio-textmining (2003, background): “‡ For detailed description on the categories, see Ohta et al. (2002). § http://www.”
An adaptive annotation approach for biomedical entity and relation recognition (2016, background): “Recent years have seen a surge on biomedical text processing (see [23] for a survey), most of which rely on the GENIA corpus [24], which is a collection of biomedical abstracts.”
DeepStruct: Pretraining of Language Models for Structure Prediction (2022, background): “• The GENIA (Ohta et al., 2002) dataset: GE-NIA consists of compiled and annotated biomedical literature, which contains 14,824 training samples, 1,855 validating samples, and 1,854 testing samples.”
Tuning support vector machines for biomedical named entity recognition (2002, background): “To overcome such a situation, the GENIA corpus (Ohta et al., 2002) has been developed, and at this time it is the largest biomedical annotated corpus available to public, containing 670 annotated abstracts of the MEDLINE database.”
Developing a Robust Part-of-Speech Tagger for Biomedical Text (2005, methodology): “The development set was used for feature selection and parameter tuning. ‐ GENIA corpus (version 3.02) [ 6 ]”
GPT-NER: Named Entity Recognition via Large Language Models (2023, methodology): “More details (including entity types, sentence number, and examples) of three nested NER datasets ACE2004, ACE2005, and GENIA can be found in Appendix A.2.”
A survey of named entity recognition and classification (2007, background): “A recent interest in bioinformatics, and the availability of the GENIA corpus (T. Ohta et al. 2002) led to many studies dedicated to types such as " protein " , " DNA " , " RNA " , " cell line " and " cell type " (e.g., D. Shen et al. 2003, B. Settles 2004) as well as studies targeted to " protein…”
Unsupervised Learning of Semantic Relations between Concepts of a Molecular Biology Ontology (2005, methodology): “We apply our system to a corpus of molecular biology literature, the GENIA corpus [Ohta et al., 2002], and generate a list of labeled binary relations between pairs of GENIA ontology concepts.”