Katsuya Masuda

Semantic Retrieval for the Accurate Identification of Relational Concepts in Massive Textbases

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.

An Intelligent Search Engine and GUI-based Efficient MEDLINE Search Tool Based on Deep Syntactic Parsing

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

A Ranking Model of Proximal and Structural Text Retrieval Based on Region Algebra

This paper investigates an application of the ranked region algebra to information retrieval from large scale but unannotated documents. We automatically annotated documents with document structure and semantic tags by using taggers, and retrieve information by specifying structure represented by tags and words using ranked region algebra. We report in detail what kind of data can be retrieved in the experiments by this approach.

A robust retrieval engine for proximal and structural search

In the text retrieval area including XML and Region Algebra, many researchers pursued models for specifying what kinds of information should appear in specified structural positions and linear positions (Chinenyanga and Kushmerick, 2001; Wolff et al., 1999; Theobald and Weilkum, 2000; Clarke et al., 1995). The models attracted many researchers because they are considered to be basic frameworks for retrieving or extracting complex information like events. However, unlike IR by keyword-based search, their models are not robust, that is, they support only exact matching of queries, while we would like to know to what degree the contents in specified structural positions are relevant to those in the query even when the structure does not exactly match the query.

Nested region algebra extended with variables for tag-annotated text search

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.