Combination and boundary detection approaches on Chinese indexing

Abstract

Digital libraries store materials in electronic format. Research and development in digital libraries includes content creation, conversion, indexing, organization, and dissemination. The key technological issues are how to search and display desired selections from and across large collections effectively [Schatz & Chen, 1996]. Digital library research projects (DLI-1) sponsored by NSF/DARPA/NASA have a common theme of bringing search to the net, which is the flagship research effort for the National Information Infrastructure (NII) in the United States. A repository is an indexed collection of objects. Indexing is an important task for searching. The better the indexing, the better the searching result. Developing a universal digital library has been the dream of many researchers, however, there are still many problems to be solved before such a vision is fulfilled. The most critical is to support a cross-lingual retrieval or multilingual digital library. Much work has been done on English information retrieval, however, there is relatively less work on Chinese information retrieval. In this article, we focus on Chinese indexing, which is the foundation of Chinese and cross-lingual information retrieval. The smallest indexing units in Chinese digital libraries are words, while the smallest units in a Chinese sentence are characters. However, Chinese text has no delimiter to mark word boundaries as it is in English text. In English or other languages using Roman or Greek-based orthographies, often, spacing reliably indicates word boundaries. In Chinese, a number of characters are placed together without any delimiters indicating the boundaries between consecutive characters. In this article, we investigate the combination and boundary detection approaches based on mutual information for segmentation. The combination approach combines n-grams to form words with more number of characters. In the combination approach Algorithm 1 does not allow overlapping of n-grams while Algorithm 2 does. The boundary detection approach detects the segmentation points on a sentence based on the values and the change of values of the mutual information. Experiments are conducted to evaluate their performances. An interface of the system is also presented to show how a Chinese web page is downloaded, the text in the page filtered, and segmented into words. The segmented words can be submitted for indexing or new unknown words can be identified and submitted to a dictionary.