A user-centred evaluation of ranking algorithms for interactive query expansion

Efthimiadis, Efthimis N.

doi:10.1145/160688.160710

Cited by 54 publications

(30 citation statements)

References 19 publications

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“…It is useful to compare our results with those reported in (Efthimiadis 1993). This is one of the few studies that compare the performance of multiple term-ranking functions for query expansion by analyzing the composition of the lists of suggested terms and not the system's overall retrieval effectiveness.…”

Section: Term-ranking Correlationmentioning

confidence: 99%

Improving retrieval feedback with multiple term-ranking function combination

Carpineto

Romano

Giannini

2002

ACM Trans. Inf. Syst.

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and VITTORIO GIANNINI WIND Telecomunicazioni S.p.A ________________________________________________________________________In this paper we consider methods for automatic query expansion from top retrieved documents (i.e., retrieval feedback) which make use of various functions for scoring expansion terms within Rocchio's classical reweighting scheme. An analytical comparison shows that the retrieval performance of methods based on distinct term-scoring functions is comparable on the whole query set but considerably differs on single queries, consistent with the fact that the ordered sets of expansion terms suggested for each query by the different functions are largely uncorrelated. Motivated by these findings, we argue that the results of multiple functions can be merged, by analogy with ensembling classifiers, and present a simple combination technique based on the rank values of the suggested terms. The combined retrieval feedback method is effective not only with respect to unexpanded queries but also to any individual method, with notable improvements on the system's precision. Furthermore, the combined method is robust with respect to variation of experimental parameters and it is beneficial even when the same information needs are expressed with shorter queries.

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Section: Term-ranking Correlationmentioning

confidence: 99%

Improving retrieval feedback with multiple term-ranking function combination

Carpineto

Romano

Giannini

2002

ACM Trans. Inf. Syst.

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“…We utilize β [1,10] scale in figures to explain the expected improvement. Specifically, we tried with β = [1,2,3,4,5,6,7,8,9,10], where each point is equal to the average performance of the 10 queries.…”

Section: ) Resultsmentioning

confidence: 99%

“…Query expansion requires a term selection stage where the system presents the query expansion terms to the users in some reasonable order [3]. The order should preferably be one in which the terms that are most likely to be useful are close to the top of the list.…”

Section: A Query Expansion In the Medical Domainmentioning

confidence: 99%

Using Tag-Neighbors for Query Expansion in Medical Information Retrieval

Durão

Bayyapu

et al. 2011

2011 International Conference on Information Science and Applications

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Abstract-In the context of medical document retrieval, users often under-specified queries lead to undesired search results that suffer from not containing the information they seek, inadequate domain knowledge matches and unreliable sources. To overcome the limitations of under-specified queries, we utilize tags to enhance information retrieval capabilities by expanding users' original queries with context-relevant information. We compute a set of significant tag neighbor candidates based on the neighbor frequency and weight, and utilize the most frequent and weighted neighbors to expand an entry query that has terms matching tags. The proposed approach is evaluated using MedWorm medical article collection and standard evaluation methods from the text retrieval conference (TREC). We compared the baseline of 0.353 for Mean Average Precision (MAP), reaching a MAP 0.491 (+39%) with the query expansion. In-depth analysis shows how this strategy is beneficial when compared with different ranks of the retrieval results. I. INTRODUCTIONIn the context of medical document retrieval, users often under-specified queries lead to undesired search results that suffer from not containing the information they seek, inadequate domain knowledge matches and unreliable sources. For instance, when a user wants to search for a recent outbreak of influenza on the web, a search with the query influenza will return a list of documents containing the query term, ranked by a set of criteria defined by the search engine. In this case, at least three issues may affect the quality of the search result. One, a query with only one or two terms may be under-specified, that is, it may not contain enough terms for the search engine to retrieve the desired information to the user. Second, in the document repository of the search engine, there might exist more than hundreds of thousands articles matching the requested query. In such an amount of information, it is impossible to locate the desired information by simply browsing through all contents of returned results. The third reason is related to domain knowledge requirements. Because conventional search engines focus on generic information search, domain specific results are usually not taken into consideration during the search. Thus, a simple word based search does not produce relevant search results in specific domains such as the medical domain [1]. As a consequence of these issues related to query-based searches, only one fourth to one half of the relevant articles on a given topic are retrieved in searches performed in specific domains [2]. In other words, the sparse and incomplete query terms may

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“…First, it uses blind relevance feedback (BRF) to expand the non-stopwords tall, buildings and usa, and weights the expanded terms with the w t (p t -q t ) algorithm [9]. Secondly, it performs geographic query expansion for geographic terms, by exploring their relationships as described in a geographic ontology [10].…”

Section: Quercolmentioning

confidence: 99%