This paper presents a framework for searching text regions with specifying annotated information in tag-annotated text by using Region Algebra. We extend the efficient algorithm for region algebra to handle both nested and crossed regions and introduce variables for attribute values to treat tag-annotations in which attributes indicate another tag regions. Our framework have been implemented in a text search engine for MEDLINE, which is a large textbase of abstracts in medical science. Experiments in tag-annotated MEDLINE abstracts demonstrate the effectiveness of specifying annotations and the efficiency of our framework.

This paper introduces a novel framework for the accurate retrieval of relational concepts from huge texts. Prior to retrieval, all sentences are annotated with predicate argument structures and ontological identifiers by applying a deep parser and a term recognizer. During the run time, user requests are converted into queries of region algebra on these annotations. Structural matching with pre-computed semantic annotations establishes the accurate and efficient retrieval of relational concepts. This framework was applied to a text retrieval system for MEDLINE. Experiments on the retrieval of biomedical correlations revealed that the cost is sufficiently small for real-time applications and that the retrieval precision is significantly improved.

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.