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Information Extraction Techniques from Biomedical Texts

This document outlines rules for extracting information from biomedical texts, highlighting the importance of the order of rules, where high-frequency appearances come first. It discusses various domains defined by verbs, such as studies related to drugs or therapeutic modalities. An example illustrates the extraction process, demonstrating how dependency parsing identifies subjects and objects in sentences. This framework aims to enhance the effectiveness of pattern matching in extracting meaningful data from complex biomedical literature.

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Information Extraction Techniques from Biomedical Texts

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  1. Information Extraction from biomedical texts Mohammed Alshayeb 11/16/2009

  2. Rules

  3. Order of Rules • High frequency appearance come first. • More evidences are needed.

  4. Domains • Domains have verbs. • e.g. studies/MM-DRUG_CHEMICAL_COMPOUND_THERAPEUTIC_MODALITY/VB • So far Pattern Matcher expects domains have only names.

  5. Example • Sentence: tpck/MM-MOLECULE/NN or L-NAME causes hemorrhagic_shock/MM-CONDITION/NN • Dependency: nsubj(causes-4, tpck/MM-MOLECULE/NN-5) -- dobj(case-4,hemorrhagic_shock/MM-CONDITION/NN-7) • Match: case-4(tpck/MM-MOLECULE/NN-5, hemorrhagic_shock/MM-CONDITION/NN-7)

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