In this paper we describe on-going work aimed at creating a dependency-based annotated treebank for the BioMedical domain. Our starting point is the GENIA corpus [14], which is a corpus of 2000 MEDLINE abstracts, which has been manually annotated for various biological entities, according

We report the construction of a corpus for parser evaluation in the biomedical domain. A 50-abstract subset (492 sentences) of the GENIA corpus (Kim et al., 2003) is annotated with labeled head-dependent relations using the grammatical relations (GR) evaluation scheme (Carroll et al., 1998),which

In this paper we describe on-going work aimed at creating a dependency-based annotated treebank for the BioMedical domain. Our starting point is the GENIA corpus [14], which is a corpus of 2000 MEDLINE abstracts, which has been manually annotated for various biological entities, according

Motivation: Annotated reference corpora such as the GENIA corpus play an important role in biomedical information extraction. A semantic annotation of the natural language texts in these reference corpora using formal ontologies and logic is challenging due to the ambiguous use of natural language

We present an outline of the genome information acquisition (GENIA) project for automatically extracting biochemical information from journal papers and ab- stracts. GENIA will be available over the Internet and is designed to aid in information extraction, retrieval and visualisation

We explore the use of Support Vector Machines (SVMs) for biomedical named entity recognition. To make the SVM training with the available largest corpus -- the GENIA corpus -- tractable, we propose to split the non-entity class into sub-classes, using part-of-speech information. In addition, we

BIOINFORMATICS Vol. 19 Suppl. 1 2003, pages i180–i182DOI: 10.1093/bioinformatics/btg1023 GENIA corpus—a semantically annotated corpus for bio-textmining

We present an outline of the genome information acquisition (GENIA) project for automatically extracting biochemical information from journal papers and abstracts. GENIA will be available over the Internet and is designed to aid in information extraction, retrieval and visualisation and to help

We explore the use of Support Vector Machines (SVMs) for biomedical named entity recognition. To make the SVM training with the available largest corpus -- the GENIA corpus -- tractable, we propose to split the non-entity class into sub-classes, using part-of-speech information. In addition

The GENIA project [9] (Fig. 1) seeks to automatically extract useful information from texts written by scientists to help overcome the problems caused by information overload. We intend that while the methods are customized for application in the micro-biology domain, the basic methods should