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There has been little research on task complexity and difficulty in music information retrieval (MIR), whereas many studies in the text retrieval domain have found that task complexity and difficulty have significant effects on user effectiveness. This study aimed to bridge the gap by exploring i) the relationship between task complexity and difficulty; ii) factors affecting task difficulty; and iii) the relationship between task difficulty, task complexity, and user search behaviors in MIR. An empirical user experiment was conducted with 51 participants and a novel MIR system. The participants searched for 6 topics across 3 complexity levels. The results revealed that i) perceived task difficulty in music search is influenced by task complexity, user background, system affordances, and task uncertainty and enjoyability; and ii) perceived task difficulty in MIR is significantly correlated with effectiveness metrics such as the number of songs found, number of clicks, and task completion time. The findings have implications for the design of music search tasks (in research) or use cases (in system development) as well as future MIR systems that can detect task difficulty based on user effectiveness metrics. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various articles within the issue on topics including Information retrieval (IR) in the intellectual property, cross-language retrieval, categorisation and image search in patents .

The application of word sense disambiguation (WSD) techniques to information retrieval (IR) has yet to provide convincing retrieval results. Major obstacles to effective WSD in IR include coverage and granularity problems of word sense inventories, sparsity of document context, and limited information provided by short queries. In this paper, to alleviate these issues, we propose the construction of latent context models for terms using latent Dirichlet allocation. We propose building one latent context per word, using a well principled representation of local context based on word features. In particular, context words are weighted using a decaying function according to their distance to the target word, which is learnt from data in an unsupervised manner. The resulting latent features are used to discriminate word contexts, so as to constrict query's semantic scope. Consistent and substantial improvements, including on difficult queries, are observed on TREC test collections, and the techniques combines well with blind relevance feedback. Compared to traditional topic modeling, WSD and positional indexing techniques, the proposed retrieval model is more effective and scales well on large-scale collections. [ABSTRACT FROM AUTHOR]

Modern information retrieval (IR) test collections have grown in size, but the available manpower for relevance assessments has more or less remained constant. Hence, how to reliably evaluate and compare IR systems using incomplete relevance data, where many documents exist that were never examined by the relevance assessors, is receiving a lot of attention. This article compares the robustness of IR metrics to incomplete relevance assessments, using four different sets of graded-relevance test collections with submitted runs—the TREC 2003 and 2004 robust track data and the NTCIR-6 Japanese and Chinese IR data from the crosslingual task. Following previous work, we artificially reduce the original relevance data to simulate IR evaluation environments with extremely incomplete relevance data. We then investigate the effect of this reduction on discriminative power, which we define as the proportion of system pairs with a statistically significant difference for a given probability of Type I Error, and on Kendall's rank correlation, which reflects the overall resemblance of two system rankings according to two different metrics or two different relevance data sets. According to these experiments, Q0, nDCG0 and AP0 proposed by Sakai are superior to bpref proposed by Buckley and Voorhees and to Rank-Biased Precision proposed by Moffat and Zobel. We also point out some weaknesses of bpref and Rank-Biased Precision by examining their formal definitions. [ABSTRACT FROM AUTHOR]

The demand for full-fledged information retrieval services via the World Wide Web has grown as the Internet has evolved. However, existing information retrieval systems for the World Wide Web have various limitations in the search functions, and in structure and scale of databases. The information retrieval system described in this paper has been operating for a real information retrieval service, providing high-level search functions via a command line and a Web interface for large-scale scholarly databases in text form with complex structures. In this paper, the authors first describe an outline of and the design of the information retrieval functions, in particular the characteristic functions including set operations, thesaurus searches, hierarchical structure searches, and unified searches of multiple databases. Next, they describe the implementation technologies of the system focusing on the features of the search engine, the session control method, the handling of large-scale databases, and the structure of servers and processes. Then, the authors evaluate the current system based on the real operation, and finally discuss the future issues and the related researches. © 2003 Wiley Periodicals, Inc. Syst Comp Jpn, 34(6): 44–58, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10015 [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports published within the issue on topics including the use of learning-to-rank techniques for document retrieval, the mining of sub-topics from various resources, and the approach for mining sub-topics descriptions.