Artificial Intelligence Lab, Department of MIS, University of Arizona / An automatic indexing and concept classification approach to a multilingual (Chinese and English) bibliographic
database is presented. We introduced a multi-linear termphrasing technique to extract concept descriptors (terms or keywords) from a Chinese-English bibliographic database. A concept space of related descriptors was then generated using a co-occurrence analysis technique. Like a man-made thesaurus, the system-generated concept space can be used to generate additional semantically-relevant terms for search. For concept classification and clustering, a variant of a Hopfield neural network was developed to cluster similar concept descriptors and to generate a small number of concept groups to represent (summarize) the subject matter of the database. The concept space
approach to information classification and retrieval has been adopted by the aupors in other scientific databases and business applications, but multilingual information retrieval presents a unique challenge. This research reports our experiment on multilingual databases.
Our system was initially developed in the MS-DOS
environment, running ETEN Chinese operating system.
For performance reasons, it was then tested on a UNIX-based
system. Due to the unique ideographic nature of the Chinese
language, a Chinese term-phrase indexing paradigm considering the ideographic characteristics of Chinese was developed as a multilingual information classification model. By applying the neural network based concept classification technique, the model presents a novel way of organizing unstructured multilingual information.
|Creators||Lin, Chung-hsin, Chen, Hsinchun|
|Source Sets||University of Arizona|
|Type||Journal Article (Paginated)|
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