A study on multi-Iingual information retrieval with structured index

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In this research, we have investigated the potentiality of a novel information retrieval method which is named "Structured Index." In this method, we generate an index represented in a binary-tree structure, which is created through a dependency analysis between words that compose titles of scientific papers. This method is expected to outperform conventional keyword-based information retrieval methods, because this index would be more appropriate for matching to the intention embedded in users' queries. Furthermore, this method can be more suitable for cross-lingual information retrieval since index is more concept-oriented. Firstly, we made a fundamental software system with Japanese language morphological analysis of paper titles and dependency analysis between words. Based on these analyzes, we investigated (1) a method appropriate for index structuring, and (2) a general algorithm for retrieval processing. After these preliminary works, we have made a practical retrieval software … More system which was applied to one of the largest Japanese test collections, NTCIR. We made a structured index for title and abstract fields and the evaluation has shown that our new method outperforms conventional methods. We also designed an approach to apply our method to English information retrieval. In terms of cross-lingual information retrieval, another method "Relevance Superimposition (RS) Model" that we also have proposed concurrently has shown better performance, and we decided to choose RS model for our cross-lingual information retrieval. Experiments on the test collections have shown that our system achieved better retrieval performance in both Japanese and English collections. The software we have implemented is composed of two parts, i. e., the language-independent part and the language-dependent part. This structure is better for further development of functionality enhancement for other languages. We also made a web-base user interface of information retrieval for the demonstration of our research achievements. Less