A comparison of indexing techniques for Japanese text retrieval
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Statistical inference in retrieval effectiveness evaluation
Information Processing and Management: an International Journal
Employing multiple representations for Chinese information retrieval
Journal of the American Society for Information Science
Experimentation as a way of life: Okapi at TREC
Information Processing and Management: an International Journal - The sixth text REtrieval conference (TREC-6)
Probabilistic models of information retrieval based on measuring the divergence from randomness
ACM Transactions on Information Systems (TOIS)
A comparison of Chinese document indexing strategies and retrieval models
ACM Transactions on Asian Language Information Processing (TALIP)
Chinese word segmentation and its effect on information retrieval
Information Processing and Management: an International Journal
Data Analysis and Graphics Using R: An Example-based Approach (Cambridge Series in Statistical and Probabilistic Mathematics)
Searching strategies for the Hungarian language
Information Processing and Management: an International Journal
When stopword lists make the difference
Journal of the American Society for Information Science and Technology
Comparative Study of Indexing and Search Strategies for the Hindi, Marathi, and Bengali Languages
ACM Transactions on Asian Language Information Processing (TALIP)
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This paper first describes various strategies (character, bigram, automatic segmentation) used to index the Chinese (ZH), Japanese (JA) and Korean (KR) languages. Second, based on the NTCIR-5 test-collections, it evaluates various retrieval models, varying from classical vector-space models to more recent developments in probabilistic and language models. While no clear conclusion was reached for the Japanese language, the bigram-based indexing strategy seems to be the best choice for Korean, and the combined ”unigram & bigram” indexing strategy is best for traditional Chinese. On the other hand, Divergence from Randomness (DFR) probabilistic model usually results in the best mean average precision. Finally, upon an evaluation of the four different statistical tests, we find that their conclusions correlate, even more when comparing the non-parametric bootstrap with the t-test.