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Graciela Gonzalez

  1. Sentence Simplification Aids Protein-Protein Interaction Extraction.

    Втр, 01/26/2010 - 16:01 — SomNambul
    Authors: Siddhartha Jonnalagadda, Graciela Gonzalez
    Subjects: Computation and Language (Computational Linguistics and Natural Language and Speech Processing)
    Abstract

    Accurate systems for extracting Protein-Protein Interactions (PPIs)
    automatically from biomedical articles can help accelerate biomedical research.
    Biomedical Informatics researchers are collaborating to provide metaservices
    and advance the state-of-art in PPI extraction. One problem often neglected by
    current Natural Language Processing systems is the characteristic complexity of
    the sentences in biomedical literature.

  2. ONER: Tool for Organization Named Entity Recognition from Affiliation Strings in PubMed Abstracts.

    Втр, 01/26/2010 - 16:01 — SomNambul
    Authors: Siddhartha Jonnalagadda, Graciela Gonzalez, Philip Topham
    Subjects: Digital Libraries
    Abstract

    Automatically extracting organization names from the affiliation sentences of
    articles related to biomedicine is of great interest to the pharmaceutical
    marketing industry, health care funding agencies and public health officials.
    It will also be useful for other scientists in normalizing author names,
    automatically creating citations, indexing articles and identifying potential
    resources or collaborators.

  3. Towards Automatic Extraction of Social Networks of Organizations in PubMed Abstracts.

    Втр, 01/26/2010 - 16:01 — SomNambul
    Authors: Siddhartha Jonnalagadda, Graciela Gonzalez, Philip Topham
    Subjects: Digital Libraries
    Abstract

    Social Network Analysis (SNA) of organizations can attract great interest
    from government agencies and scientists for its ability to boost translational
    research and accelerate the process of converting research to care. For SNA of
    a particular disease area, we need to identify the key research groups in that
    area by mining the affiliation information from PubMed. This not only involves
    recognizing the organization names in the affiliation string, but also
    resolving ambiguities to identify the article with a unique organization.

  4. Towards Effective Sentence Simplification for Automatic Processing of Biomedical Text.

    Втр, 01/26/2010 - 16:01 — SomNambul
    Authors: Siddhartha Jonnalagadda, Graciela Gonzalez, Luis Tari, Jorg Hakenberg, Chitta Baral
    Subjects: Computation and Language (Computational Linguistics and Natural Language and Speech Processing)
    Abstract

    The complexity of sentences characteristic to biomedical articles poses a
    challenge to natural language parsers, which are typically trained on
    large-scale corpora of non-technical text. We propose a text simplification
    process, bioSimplify, that seeks to reduce the complexity of sentences in
    biomedical abstracts in order to improve the performance of syntactic parsers
    on the processed sentences. Syntactic parsing is typically one of the first
    steps in a text mining pipeline. Thus, any improvement in performance would
    have a ripple effect over all processing steps.

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