Report on CLIR task for the NTCIR-5 evaluation campaign

Lecture Notes in Computer Science, 2010

A distinctive feature of Chinese test is that a Chinese document is a sequence of Chinese with no space or boundary between Chinese words. This feature makes Chinese information retrieval more difficult since a retrieved document which contains the query term as a sequence of Chinese characters may not be really relevant to the query since the query term (as a sequence Chinese characters) may not be a valid Chinese word in that documents. On the other hand, a document that is actually relevant may not be retrieved because it does not contain the query sequence but contains other relevant words. In this research, we propose a hybrid Chinese information retrieval model by incorporating wordbased techniques with the traditional character-based techniques. The aim of this approach is to investigate the influence of Chinese segmentation on the performance of Chinese information retrieval. Two ranking methods are proposed to rank retrieved documents based on the relevancy to the query calculated by combining character-based ranking and word-based ranking. Our experimental results show that Chinese segmentation can improve the performance of Chinese information retrieval, but the improvement is not significant if it incorporates only Chinese segmentation with the traditional character-based approach.

ACM SIGIR Forum, 1997

It is generafly believed that words, rather than characters, should be the smallest indexing unit for Chinese text retrieval systems, and that it is essential to have a comprehensive Chinese dictionary or lexicon for Chhmse text retrieval systems to do well. Chinese text has no delimiters to mark woni boundaries. As a result, any text retrieval systems that build word-based indexes need to segment text into words. We implemented several statistical and dictionary-hazed word segmentation methods to study the effect on retrieval effectiveness of different segmentation methods using the TREC-S Chinese test collection and topics. The results show that, for all three sets of queries, the simple bigram indexing and the purely statistical word segmentation perform better than the popular dictionary-based maximum matching method with a dictionary of 138,955 entries.

2004

Traditional information retrieval models treat the query as a bag of words, assuming that the occurrence of each query term is independent of the positions and occurrences of others. Several of these traditional models have been extended to incorporate positional information, most often through the inclusion of phrases. This has shown improvements in effectiveness on large, modern test collections. The language modeling approach to information retrieval is attractive because it provides a well-studied theoretical framework that has been successful in other fields. Incorporating positional information into language models is intuitive and has shown significant improvements in several language-modeling applications. However, attempts to integrate positional information into the language-modeling approach to IR have not shown consistent significant improvements. This paper provides a broader exploration of this problem. We apply the backoff technique to incorporate a bigram phrase lang...

2003

Information retrieval (IR) research has reached a point where it is appropriate to assess progress and to define a research agenda for the next five to ten years. This report summarizes a discussion of IR research challenges that took place at a recent workshop. The attendees of the workshop considered information retrieval research in a range of areas chosen to give broad coverage of topic areas that engage information retrieval researchers.

Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '96, 1996

In previous studies, Chinese text retrieval has often been dealt with on the character basis. This approach is not suited to deal with complex queries. We suggest that Chirmse text retrieval should work with words inslead of characters. The crucial problem is to segment originally continuous Chinese texts into words. In this paper, wc Ilrsi propose a hybrid segmentation approach which unifies the commonly used approaches. The systcm SMART is then udaptcd to index the segmented Chinese texls. Finally, wc suggest that Chinese text retrieval should move further to include a thesaurus in order to cope with dle rich vocabulary of Chinese.

2006

For our participation in this CLEF evaluation campaign, the first objective was to propose and evaluate various indexing and search strategies for the Hungarian language in order to produce better retrieval effectiveness than language-independent approach (n-gram). Using both a new stemmer including some derivational suffixes removals, and a more aggressive automatic decompounding scheme, we were able to produce better retrieval effectiveness than corresponding 4-gram indexing scheme. Our second objective was to obtain a better picture of the relative merit of various search engines with the French, Brazilian/Portuguese and Bulgarian languages. To do so we evaluated these test-collections using the Okapi, Divergence from Randomness (DFR) and language model (LM) models together with nine vector-processing approaches. After pseudorelevance feedback, either the DFR or the LM approach tends to produce the best IR performance. For the Bulgarian language, we also found that word-based ind...

Sigir Forum, 1999

Lecture Notes in Computer Science, 2006

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 testcollections, 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.

2007

This paper describes our Korean-Chinese cross-language information retrieval system for NTCIR-6. Our system uses a bilingual dictionary to perform query translation. We expand our bilingual dictionary by extracting words and their translations from the Wikipedia site, an online encyclopedia. To resolve the problem of translating Western people's names into Chinese, we propose a transliteration mapping method. We translate queries form Korean query to Chinese by using a co-occurrence method. When evaluating on the NTCIR-6 test set, the performance of our system achieves a mean average precision (MAP) of 0.1392 (relax score) for title query type and 0.1274 (relax score) for description query type.