GENIA corpus version 3.0 consisting of 2000 MEDLINE abstracts has been released with more than 400,000 words and almost 100,000 annotations for biological terms.

We describe here the JNLPBA shared task of bio-entity recognition using an extended version of the GENIA version 3 named entity corpus of MEDLINE abstracts. We provide background information on the task and present a general discussion of the approaches taken by participating systems.

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Background: Associating literature with pathways poses new challenges to the Text Mining (TM) community. There are three main challenges to this task: (1) the identification of the mapping position of a specific entity or reaction in a given pathway, (2) the recognition of the causal relationships among multiple reactions, and (3) the formulation and implementation of required inferences based on biological domain knowledge.

We present a practical HPSG parser for English, an intelligent search engine to retrieve MEDLINE abstracts that represent biomedical events and an efficient MED-LINE search tool helping users to find information about biomedical entities such as genes, proteins, and the interactions between them.

This paper presents a method of automatically constructing information extraction patterns on predicate-argument structures (PASs) obtained by full parsing from a smaller training corpus. Because PASs represent generalized structures for syntactical variants, patterns on PASs are expected to be more generalized than those on surface words. In addition, patterns are divided into components to improve recall and we introduce a Support Vector Machine to learn a prediction model using pattern matching results. In this paper, we present experimental results and analyze them on how well protein-protein interactions were extracted from MEDLINE abstracts. The results demonstrated that our method improved accuracy compared to a machine learning approach using surface word/part-of-speech patterns.

With the information overload in genome-related field, there is an infreest 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.

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