-
Uratani, N.; Takeda, M.: ¬A fast string-searching algorithm for multiple patterns (1993)
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- Abstract
- The string-searching problem is to find all occurrences of pattern(s) in a text string. The Aho-Corasick string searching algorithm simultaneously finds all occurrences of multiple patterns in one pass through the text. The Boyer-Moore algorithm is the fastest algorithm for a single pattern. By combining the ideas of these two algorithms, presents an efficient string searching algorithm for multiple patterns. The algorithm runs in sublinear time, on the average, as the BM algorithm achieves, and its preprocessing time is linear proportional to the sum of the lengths of the patterns like the AC algorithm
- Source
- Information processing and management. 29(1993) no.6, S.775-791
-
Bodoff, D.; Enache, D.; Kambil, A.; Simon, G.; Yukhimets, A.: ¬A unified maximum likelihood approach to document retrieval (2001)
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- Abstract
- Empirical work shows significant benefits from using relevance feedback data to improve information retrieval (IR) performance. Still, one fundamental difficulty has limited the ability to fully exploit this valuable data. The problem is that it is not clear whether the relevance feedback data should be used to train the system about what the users really mean, or about what the documents really mean. In this paper, we resolve the question using a maximum likelihood framework. We show how all the available data can be used to simultaneously estimate both documents and queries in proportions that are optimal in a maximum likelihood sense. The resulting algorithm is directly applicable to many approaches to IR, and the unified framework can help explain previously reported results as well as guidethe search for new methods that utilize feedback data in IR
- Date
- 29. 9.2001 17:52:51
-
Soulier, L.; Jabeur, L.B.; Tamine, L.; Bahsoun, W.: On ranking relevant entities in heterogeneous networks using a language-based model (2013)
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- Abstract
- A new challenge, accessing multiple relevant entities, arises from the availability of linked heterogeneous data. In this article, we address more specifically the problem of accessing relevant entities, such as publications and authors within a bibliographic network, given an information need. We propose a novel algorithm, called BibRank, that estimates a joint relevance of documents and authors within a bibliographic network. This model ranks each type of entity using a score propagation algorithm with respect to the query topic and the structure of the underlying bi-type information entity network. Evidence sources, namely content-based and network-based scores, are both used to estimate the topical similarity between connected entities. For this purpose, authorship relationships are analyzed through a language model-based score on the one hand and on the other hand, non topically related entities of the same type are detected through marginal citations. The article reports the results of experiments using the Bibrank algorithm for an information retrieval task. The CiteSeerX bibliographic data set forms the basis for the topical query automatic generation and evaluation. We show that a statistically significant improvement over closely related ranking models is achieved.
- Date
- 22. 3.2013 19:34:49
-
Ayadi, H.; Torjmen-Khemakhem, M.; Daoud, M.; Xiangji Huang, J.; Ben Jemaa, M.: MF-Re-Rank : a modality feature-based re-ranking model for medical image retrieval (2018)
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- Abstract
- One of the main challenges in medical image retrieval is the increasing volume of image data, which render it difficult for domain experts to find relevant information from large data sets. Effective and efficient medical image retrieval systems are required to better manage medical image information. Text-based image retrieval (TBIR) was very successful in retrieving images with textual descriptions. Several TBIR approaches rely on models based on bag-of-words approaches, in which the image retrieval problem turns into one of standard text-based information retrieval; where the meanings and values of specific medical entities in the text and metadata are ignored in the image representation and retrieval process. However, we believe that TBIR should extract specific medical entities and terms and then exploit these elements to achieve better image retrieval results. Therefore, we propose a novel reranking method based on medical-image-dependent features. These features are manually selected by a medical expert from imaging modalities and medical terminology. First, we represent queries and images using only medical-image-dependent features such as image modality and image scale. Second, we exploit the defined features in a new reranking method for medical image retrieval. Our motivation is the large influence of image modality in medical image retrieval and its impact on image-relevance scores. To evaluate our approach, we performed a series of experiments on the medical ImageCLEF data sets from 2009 to 2013. The BM25 model, a language model, and an image-relevance feedback model are used as baselines to evaluate our approach. The experimental results show that compared to the BM25 model, the proposed model significantly enhances image retrieval performance. We also compared our approach with other state-of-the-art approaches and show that our approach performs comparably to those of the top three runs in the official ImageCLEF competition.
- Date
- 29. 9.2018 11:43:31
-
Maron, M.E.: ¬An historical note on the origins of probabilistic indexing (2008)
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- Abstract
- The motivation behind "Probabilistic Indexing" was to replace two-valued thinking about information retrieval with probabilistic notions. This involved a new view of the information retrieval problem - viewing it as problem of inference and prediction, and introducing probabilistically weighted indexes and probabilistically ranked output. These ideas were first formulated and written up in August 1958.
-
Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986)
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- Source
- Information processing and management. 22(1986) no.6, S.465-476
-
Sachs, W.M.: ¬An approach to associative retrieval through the theory of fuzzy sets (1976)
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- Abstract
- The theory of fuzzy sets is used to provide a rogorous formulation of the problem of associative retrieval. This formulation suggests the idea of using fuzzy clustering to organize data for retrieval
-
Cheng, C.-S.; Chung, C.-P.; Shann, J.J.-J.: Fast query evaluation through document identifier assignment for inverted file-based information retrieval systems (2006)
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- Abstract
- Compressing an inverted file can greatly improve query performance of an information retrieval system (IRS) by reducing disk I/Os. We observe that a good document identifier assignment (DIA) can make the document identifiers in the posting lists more clustered, and result in better compression as well as shorter query processing time. In this paper, we tackle the NP-complete problem of finding an optimal DIA to minimize the average query processing time in an IRS when the probability distribution of query terms is given. We indicate that the greedy nearest neighbor (Greedy-NN) algorithm can provide excellent performance for this problem. However, the Greedy-NN algorithm is inappropriate if used in large-scale IRSs, due to its high complexity O(N2 × n), where N denotes the number of documents and n denotes the number of distinct terms. In real-world IRSs, the distribution of query terms is skewed. Based on this fact, we propose a fast O(N × n) heuristic, called partition-based document identifier assignment (PBDIA) algorithm, which can efficiently assign consecutive document identifiers to those documents containing frequently used query terms, and improve compression efficiency of the posting lists for those terms. This can result in reduced query processing time. The experimental results show that the PBDIA algorithm can yield a competitive performance versus the Greedy-NN for the DIA problem, and that this optimization problem has significant advantages for both long queries and parallel information retrieval (IR).
-
Na, S.-H.; Kang, I.-S.; Roh, J.-E.; Lee, J.-H.: ¬An empirical study of query expansion and cluster-based retrieval in language modeling approach (2007)
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- Abstract
- The term mismatch problem in information retrieval is a critical problem, and several techniques have been developed, such as query expansion, cluster-based retrieval and dimensionality reduction to resolve this issue. Of these techniques, this paper performs an empirical study on query expansion and cluster-based retrieval. We examine the effect of using parsimony in query expansion and the effect of clustering algorithms in cluster-based retrieval. In addition, query expansion and cluster-based retrieval are compared, and their combinations are evaluated in terms of retrieval performance by performing experimentations on seven test collections of NTCIR and TREC.
-
Sánchez-de-Madariaga, R.; Fernández-del-Castillo, J.R.: ¬The bootstrapping of the Yarowsky algorithm in real corpora (2009)
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- Abstract
- The Yarowsky bootstrapping algorithm resolves the homograph-level word sense disambiguation (WSD) problem, which is the sense granularity level required for real natural language processing (NLP) applications. At the same time it resolves the knowledge acquisition bottleneck problem affecting most WSD algorithms and can be easily applied to foreign language corpora. However, this paper shows that the Yarowsky algorithm is significantly less accurate when applied to domain fluctuating, real corpora. This paper also introduces a new bootstrapping methodology that performs much better when applied to these corpora. The accuracy achieved in non-domain fluctuating corpora is not reached due to inherent domain fluctuation ambiguities.
-
Archuby, C.G.: Interfaces se recuperacion para catalogos en linea con salidas ordenadas por probable relevancia (2000)
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- Date
- 29. 1.1996 18:23:13
- Source
- Ciencia da informacao. 29(2000) no.3, S.5-13
-
Crestani, F.: Combination of similarity measures for effective spoken document retrieval (2003)
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- Source
- Journal of information science. 29(2003) no.2, S.87-96
-
Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983)
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- Date
- 30. 3.2001 13:32:22
-
Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000)
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- Date
- 25. 8.2005 17:42:22
-
Okada, M.; Ando, K.; Lee, S.S.; Hayashi, Y.; Aoe, J.I.: ¬An efficient substring search method by using delayed keyword extraction (2001)
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- Date
- 29. 3.2002 17:24:03
-
Cole, C.: Intelligent information retrieval: diagnosing information need : Part II: uncertainty expansion in a prototype of a diagnostic IR tool (1998)
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- Date
- 11. 8.2001 14:48:29
-
Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986)
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- Date
- 14. 6.2015 22:12:44
-
Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986)
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- Date
- 14. 6.2015 22:12:56
-
Frants, V.I.; Shapiro, J.: Control and feedback in a documentary information retrieval system (1991)
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- Abstract
- Addresses the problem of control in documentary information retrieval systems is analysed and it is shown why an IR system has to be looked at as an adaptive system. The algorithms of feedback are proposed and it is shown how they depend on the type of the collection of documents: static (no change in the collection between searches) and dynamic (when the change occurs between searches). The proposed algorithms are the basis for the development of the fully automated information retrieval systems
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Smith, M.; Smith, M.P.; Wade, S.J.: Applying genetic programming to the problem of term weight algorithms (1995)
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