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Schiminovich, S.: Automatic classification and retrieval of documents by means of a bibliographic pattern discovery algorithm (1971)
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Ciocca, G.; Schettini, R.: ¬A relevance feedback mechanism for content-based image retrieval (1999)
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Bovey, J.D.; Robertson, S.E.: ¬An algorithm for weighted searching on a Boolean system (1984)
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Aho, A.; Corasick, M.: Efficient string matching : an aid to bibliographic search (1975)
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Boyer, R.; Moore, S.: ¬A fast string searching algorithm (1977)
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Kwok, K.L.: Improving English and Chinese ad-hoc retrieval : a TIPSTER text phase 3 project report (2000)
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Koyama, M.: ¬A fast retrieving algorithm of hierarchical relationships using tree structures (1998)
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Hofferer, M.: Heuristic search in information retrieval (1994)
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- Abstract
- Describes an adaptive information retrieval system: Information Retrieval Algorithm System (IRAS); that uses heuristic searching to sample a document space and retrieve relevant documents according to users' requests; and also a learning module based on a knowledge representation system and an approximate probabilistic characterization of relevant documents; to reproduce a user classification of relevant documents and to provide a rule controlled ranking
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Watters, C.; Amoudi, A.: Geosearcher : location-based ranking of search engine results (2003)
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- Abstract
- Waters and Amoudi describe GeoSearcher, a prototype ranking program that arranges search engine results along a geo-spatial dimension without the provision of geo-spatial meta-tags or the use of geo-spatial feature extraction. GeoSearcher uses URL analysis, IptoLL, Whois, and the Getty Thesaurus of Geographic Names to determine site location. It accepts the first 200 sites returned by a search engine, identifies the coordinates, calculates their distance from a reference point and ranks in ascending order by this value. For any retrieved site the system checks if it has already been located in the current session, then sends the domain name to Whois to generate a return of a two letter country code and an area code. With no success the name is stripped one level and resent. If this fails the top level domain is tested for being a country code. Any remaining unmatched names go to IptoLL. Distance is calculated using the center point of the geographic area and a provided reference location. A test run on a set of 100 URLs from a search was successful in locating 90 sites. Eighty three pages could be manually found and 68 had sufficient information to verify location determination. Of these 65 ( 95%) had been assigned reasonably correct geographic locations. A random set of URLs used instead of a search result, yielded 80% success.
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Maron, M.E.; Kuhns, I.L.: On relevance, probabilistic indexing and information retrieval (1960)
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- Abstract
- Reports on a novel technique for literature indexing and searching in a mechanized library system. The notion of relevance is taken as the key concept in the theory of information retrieval and a comparative concept of relevance is explicated in terms of the theory of probability. The resulting technique called 'Probabilistic indexing' allows a computing machine, given a request for information, to make a statistical inference and derive a number (called the 'relevance number') for each document, which is a measure of the probability that the document will satisfy the given request. The result of a search is an ordered list of those documents which satisfy the request ranked according to their probable relevance. The paper goes on to show that whereas in a conventional library system the cross-referencing ('see' and 'see also') is based soley on the 'semantic closeness' between index terms, statistical measures of closeness between index terms can be defined and computed. Thus, given an arbitrary request consisting of one (or many) index term(s), a machine can eleborate on it to increase the probability of selecting relevant documents that would not otherwise have been selected. Finally, the paper suggest an interpretation of the whole library problem as one where the request is considered as a clue on the basis of which the library system makes a concatenated statistical inference in order to provide as an output an ordered list of those documents which most probably satisfy the information needs of the user
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Wan, X.; Yang, J.; Xiao, J.: Towards a unified approach to document similarity search using manifold-ranking of blocks (2008)
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- Abstract
- Document similarity search (i.e. query by example) aims to retrieve a ranked list of documents similar to a query document in a text corpus or on the Web. Most existing approaches to similarity search first compute the pairwise similarity score between each document and the query using a retrieval function or similarity measure (e.g. Cosine), and then rank the documents by the similarity scores. In this paper, we propose a novel retrieval approach based on manifold-ranking of document blocks (i.e. a block of coherent text about a subtopic) to re-rank a small set of documents initially retrieved by some existing retrieval function. The proposed approach can make full use of the intrinsic global manifold structure of the document blocks by propagating the ranking scores between the blocks on a weighted graph. First, the TextTiling algorithm and the VIPS algorithm are respectively employed to segment text documents and web pages into blocks. Then, each block is assigned with a ranking score by the manifold-ranking algorithm. Lastly, a document gets its final ranking score by fusing the scores of its blocks. Experimental results on the TDT data and the ODP data demonstrate that the proposed approach can significantly improve the retrieval performances over baseline approaches. Document block is validated to be a better unit than the whole document in the manifold-ranking process.
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-
Calegari, S.; Sanchez, E.: Object-fuzzy concept network : an enrichment of ontologies in semantic information retrieval (2008)
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- Abstract
- This article shows how a fuzzy ontology-based approach can improve semantic documents retrieval. After formally defining a fuzzy ontology and a fuzzy knowledge base, a special type of new fuzzy relationship called (semantic) correlation, which links the concepts or entities in a fuzzy ontology, is discussed. These correlations, first assigned by experts, are updated after querying or when a document has been inserted into a database. Moreover, in order to define a dynamic knowledge of a domain adapting itself to the context, it is shown how to handle a tradeoff between the correct definition of an object, taken in the ontology structure, and the actual meaning assigned by individuals. The notion of a fuzzy concept network is extended, incorporating database objects so that entities and documents can similarly be represented in the network. Information retrieval (IR) algorithm, using an object-fuzzy concept network (O-FCN), is introduced and described. This algorithm allows us to derive a unique path among the entities involved in the query to obtain maxima semantic associations in the knowledge domain. Finally, the study has been validated by querying a database using fuzzy recall, fuzzy precision, and coefficient variant measures in the crisp and fuzzy cases.
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-
Lee, C.; Lee, G.G.: Probabilistic information retrieval model for a dependence structured indexing system (2005)
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- Abstract
- Most previous information retrieval (IR) models assume that terms of queries and documents are statistically independent from each other. However, conditional independence assumption is obviously and openly understood to be wrong, so we present a new method of incorporating term dependence into a probabilistic retrieval model by adapting a dependency structured indexing system using a dependency parse tree and Chow Expansion to compensate the weakness of the assumption. In this paper, we describe a theoretic process to apply the Chow Expansion to the general probabilistic models and the state-of-the-art 2-Poisson model. Through experiments on document collections in English and Korean, we demonstrate that the incorporation of term dependences using Chow Expansion contributes to the improvement of performance in probabilistic IR systems.
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Torra, V.; Miyamoto, S.; Lanau, S.: Exploration of textual document archives using a fuzzy hierarchical clustering algorithm in the GAMBAL system (2005)
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- Abstract
- The Internet, together with the large amount of textual information available in document archives, has increased the relevance of information retrieval related tools. In this work we present an extension of the Gambal system for clustering and visualization of documents based on fuzzy clustering techniques. The tool allows to structure the set of documents in a hierarchical way (using a fuzzy hierarchical structure) and represent this structure in a graphical interface (a 3D sphere) over which the user can navigate. Gambal allows the analysis of the documents and the computation of their similarity not only on the basis of the syntactic similarity between words but also based on a dictionary (Wordnet 1.7) and latent semantics analysis.
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Dang, E.K.F.; Luk, R.W.P.; Allan, J.: ¬A retrieval model family based on the probability ranking principle for ad hoc retrieval (2022)
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- Abstract
- Many successful retrieval models are derived based on or conform to the probability ranking principle (PRP). We present a new derivation of a document ranking function given by the probability of relevance of a document, conforming to the PRP. Our formulation yields a family of retrieval models, called probabilistic binary relevance (PBR) models, with various instantiations obtained by different probability estimations. By extensive experiments on a range of TREC collections, improvement of the PBR models over some established baselines with statistical significance is observed, especially in the large Clueweb09 Cat-B collection.
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Silva, R.M.; Gonçalves, M.A.; Veloso, A.: ¬A Two-stage active learning method for learning to rank (2014)
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- Abstract
- Learning to rank (L2R) algorithms use a labeled training set to generate a ranking model that can later be used to rank new query results. These training sets are costly and laborious to produce, requiring human annotators to assess the relevance or order of the documents in relation to a query. Active learning algorithms are able to reduce the labeling effort by selectively sampling an unlabeled set and choosing data instances that maximize a learning function's effectiveness. In this article, we propose a novel two-stage active learning method for L2R that combines and exploits interesting properties of its constituent parts, thus being effective and practical. In the first stage, an association rule active sampling algorithm is used to select a very small but effective initial training set. In the second stage, a query-by-committee strategy trained with the first-stage set is used to iteratively select more examples until a preset labeling budget is met or a target effectiveness is achieved. We test our method with various LETOR benchmarking data sets and compare it with several baselines to show that it achieves good results using only a small portion of the original training sets.
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-
Dominich, S.; Skrop, A.: PageRank and interaction information retrieval (2005)
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- Abstract
- The PageRank method is used by the Google Web search engine to compute the importance of Web pages. Two different views have been developed for the Interpretation of the PageRank method and values: (a) stochastic (random surfer): the PageRank values can be conceived as the steady-state distribution of a Markov chain, and (b) algebraic: the PageRank values form the eigenvector corresponding to eigenvalue 1 of the Web link matrix. The Interaction Information Retrieval (1**2 R) method is a nonclassical information retrieval paradigm, which represents a connectionist approach based an dynamic systems. In the present paper, a different Interpretation of PageRank is proposed, namely, a dynamic systems viewpoint, by showing that the PageRank method can be formally interpreted as a particular case of the Interaction Information Retrieval method; and thus, the PageRank values may be interpreted as neutral equilibrium points of the Web.
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-
Efron, M.: Linear time series models for term weighting in information retrieval (2010)
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- Abstract
- Common measures of term importance in information retrieval (IR) rely on counts of term frequency; rare terms receive higher weight in document ranking than common terms receive. However, realistic scenarios yield additional information about terms in a collection. Of interest in this article is the temporal behavior of terms as a collection changes over time. We propose capturing each term's collection frequency at discrete time intervals over the lifespan of a corpus and analyzing the resulting time series. We hypothesize the collection frequency of a weakly discriminative term x at time t is predictable by a linear model of the term's prior observations. On the other hand, a linear time series model for a strong discriminators' collection frequency will yield a poor fit to the data. Operationalizing this hypothesis, we induce three time-based measures of term importance and test these against state-of-the-art term weighting models.
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-
Al-Hawamdeh, S.; Smith, G.; Willett, P.; Vere, R. de: Using nearest-neighbour searching techniques to access full-text documents (1991)
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- Abstract
- Summarises the results to date of a continuing programme of research at Sheffield Univ. to investigate the use of nearest-neighbour retrieval algorithms for full text searching. Given a natural language query statement, the research methods result in a ranking of the paragraphs comprising a full text document in order of decreasing similarity with the query, where the similarity for each paragraph is determined by the number of keyword stems that it has in common with the query
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- a
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Robertson, M.; Willett, P.: ¬An upperbound to the performance of ranked output searching : optimal weighting of query terms using a genetic algorithms (1996)
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- Abstract
- Describes the development of a genetic algorithm (GA) for the assignment of weights to query terms in a ranked output document retrieval system. The GA involves a fitness function that is based on full relevance information, and the rankings resulting from the use of these weights are compared with the Robertson-Sparck Jones F4 retrospective relevance weight
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