OpenRaiser/Intern-Atlas huggingface dataset:
Hill climbing algorithms for content-based retrieval of similar configurations
Authors: Dimitris Papadias
Venue: SIGIR
Year: 2000

The retrieval of stored images matching an input configuration is an important form of content-based retrieval. Exhaustive processing (i.e., retrieval of the best solutions) of configuration similarity queries is, in general, exponential and fast search for sub-optimal solutions is the only way to deal with the vast (and ever increasing) amounts of multimedia information in several real-time applications. In this paper we discuss the utilization of hill climbing heuristics that can provide very good results within limited processing time. We propose several heuristics, which differ on the way that they search through the solution space, and identify the best ones depending on the query and image characteristics. Finally we develop new algorithms that take advantage of the specific structure of the problem to improve performance.
The impact of database selection on distributed searching
Authors: Allison L. Powell, James C. French, James P. Callan, Margaret E. Connell, Charles L. Viles
Venue: SIGIR
Year: 2000

Abstract The proliferation of online information resources increases the importance of effective and efficient distributed searching. Distributed searching is cast in three parts – database selection, query processing, and results merging. In this paper we examine the effect of database selection on retrieval performance. We look at retrieval performance in three different distributed retrieval testbeds and distill some general results. First we find that good database selection can result in better retrieval effectiveness than can be achieved in a centralized database. Second we find that good performance can be achieved when only a few sites are selected and that the performance generally increases as more sites are selected. Finally we find that when database selection is employed, it is not necessary to maintain collection wide information (CWI), e.g. global idf. Local information can be used to achieve superior performance. This means that distributed systems can be engineered with more autonomy and less cooperation. This work suggests that improvements in database selection can lead to broader improvements in retrieval performance, even in centralized (i.e. single database) systems. Given a centralized database and a good selection mechanism, retrieval performance can be improved by decomposing that database conceptually and employing a selection step. 1
INSYDER - an information assistant for business intelligence
Authors: Harald Reiterer, Gabriela Mußler, Thomas M. Mann, Siegfried Handschuh
Venue: SIGIR
Year: 2000

The WWW is the most important resource for external business information. This paper presents a tool called INSYDER, an information assistant for finding and analysis business information from the WWW. INSYDER is a system using different agents for crawling the Web, evaluating and visualising the results. These agents, the used visualisations, and a first summary of user studies are presented.
Modelling question-response patterns by scalling and visualization
Authors: Mark E. Rorvig
Venue: SIGIR
Year: 2000

The evaluation of question difficulty is usually considered the domain of Latent Trait Theory. However, these methods require standardized question sets normalized by large populations, rendering them inefficient for use in the numerous areas where questions must be evaluated. A new technique is illustrated that models the question-response cycle well, but without the procedural difficulty of the traditional methods.
Link-based and content-based evidential information in a belief network model
Authors: Ilmério Silva, Berthier A. Ribeiro-Neto, Pável Calado, Edleno Silva de Moura, Nivio Ziviani
Venue: SIGIR
Year: 2000

This work presents an information retrieval model developed to deal with hyperlinked environments. The model is based on belief networks and provides a framework for combining information extracted from the content of the documents with information derived from cross-references among the documents. The information extracted from the content of the documents is based on statistics regarding the keywords in the collection and is one of the basis for traditional information retrieval (IR) ranking algorithms. The information derived from cross-references among the documents is based on link references in a hyperlinked environment and has received increased attention lately due to the success of the Web. We discuss a set of strategies for combining these two types of sources of evidential information and experiment with them using a reference collection extracted from the Web. The results show that this type of combination can improve the retrieval performance without requiring any extra information from the users at query time. In our experiments, the improvements reach up to 59% in terms of average precision figures.
Document clustering using word clusters via the information bottleneck method
Authors: Noam Slonim, Naftali Tishby
Venue: SIGIR
Year: 2000

We present a novel implementation of the recently introduced information bottleneck method for unsupervised document clustering. Given a joint empirical distribution of words and documents, p(x, y), we first cluster the words, Y, so that the obtained word clusters, Ytilde;, maximally preserve the information on the documents. The resulting joint distribution. p(X, Ytilde;), contains most of the original information about the documents, I(X; Ytilde;) ≈ I(X; Y), but it is much less sparse and noisy. Using the same procedure we then cluster the documents, X, so that the information about the word-clusters is preserved. Thus, we first find word-clusters that capture most of the mutual information about to set of documents, and then find document clusters, that preserve the information about the word clusters. We tested this procedure over several document collections based on subsets taken from the standard 20Newsgroups corpus. The results were assessed by calculating the correlation between the document clusters and the correct labels for these documents. Finding from our experiments show that this double clustering procedure, which uses the information bottleneck method, yields significantly superior performance compared to other common document distributional clustering algorithms. Moreover, the double clustering procedure improves all the distributional clustering methods examined here.
Collaborative filtering and the generalized vector space model
Authors: Ian Soboroff, Charles K. Nicholas
Venue: SIGIR
Year: 2000

Collaborative filtering is a technique for recommending documents to users based on how similar their tastes are to other users. If two users tend to agree on what they like, the system will recommend the same documents to them. The generalized vector space model of information retrieval represents a document by a vector of its similarities to all other documents. The process of collaborative filtering is nearly identical to the process of retrieval using GVSM in a matrix of user ratings. Using this observation, a model for filtering collaboratively using document content is possible.
A novel method for the evaluation of Boolean query effectiveness across a wide operational range
Authors: Eero Sormunen
Venue: SIGIR
Year: 2000

Traditional methods for the system-oriented evaluation of Boolean IR system suffer from validity and reliability problems. Laboratory-based research neglects the searcher and studies suboptimal queries. Research on operational systems fails to make a distinction between searcher performance and system performance. This approach is neither capable of measuring performance at standard points of operation (e.g. across R0.0-R1.0).
Structured translation for cross-language information retrieval
Authors: Ruth Sperer, Douglas W. Oard
Venue: SIGIR
Year: 2000

The paper introduces a query translation model that reflects the structure of the cross-language information retrieval task. The model is based on a structured bilingual dictionary in which the translations of each term are clustered into groups with distinct meanings. Query translation is modeled as a two-stage process, with the system first determining the intended meaning of a query term and then selecting translations appropriate to that meaning that might appear in the document collection. An implementation of structured translation based on automatic dictionary clustering is described and evaluated by using Chinese queries to retrieve English documents. Structured translation achieved an average precision that was statistically indistinguishable from Pirkola's technique for very short queries, but Pirkola's technique outperformed structured translation on long queries. The paper concludes with some observations on future work to improve retrieval effectiveness and on other potential uses of structured translation in interactive cross-language retrieval applications.
Phonetic confusion matrix based spoken document retrieval
Authors: Savitha Srinivasan, Dragutin Petkovic
Venue: SIGIR
Year: 2000

Combined word-based index and phonetic indexes have been used to improve the performance of spoken document retrieval systems primarily by addressing the out-of-vocabulary retrieval problem. However, a known problem with phonetic recognition is its limited accuracy in comparison with word level recognition. We propose a novel method for phonetic retrieval in the CueVideo system based on the probabilistic formulation of term weighting using phone confusion data in a Bayesian framework. We evaluate this method of spoken document retrieval against word-based retrieval for the search levels identified in a realistic video-based distributed learning setting. Using our test data, we achieved an average recall of 0.88 with an average precision of 0.69 for retrieval of out-of-vocabulary words on phonetic transcripts with 35% word error rate. For in-vocabulary words, we achieved a 17% improvement in recall over word-based retrieval with a 17% loss in precision for word error rites ranging from 35 to 65%.
Variance based classifier comparison in text categorization
Authors: Atsuhiro Takasu, Kenro Aihara
Venue: SIGIR
Year: 2000

Text categorization is one of the key functions for utilizing vast amount of documents. It can be seen as a classification problem, which has been studied in pattern recognition and machine learning fields for a long time and several classification methods have been developed such as statistical classification, decision tree, support vector machines and so on. Many researchers applied those classification methods to text categorization and reported their performance (e.g., decision tree[3], Bayes classifier[2], support vector machine[l]). Yang conducted comprehensive study of comparison or text categorization and reported that k nearest neighbor and support vector machines works well for text categorization[4].In the previous studies, classification methods were usually compared using single pair of training and test data However, classification method with more complex family of classifiers requires more training data and small training data may result in deriving unreliable classifier, that is, the performance of the derived classifier varies much depending on training data. Therefore, we need to take the size of training data into account when comparing and selecting a classification method. In this paper, we discuss how to select a classifier from those derived by various classification methods and how the size of training data affects the performance of the derived classifier.In order to evaluate the reliability of classification method, we consider the variance of accuracy of derived classifier. We first construct a statistical model. In the text categorization, each document is usually represented with a feature vector that consists of weighted frequencies of terms. In the vector space model, document is a point in high dimensional feature space and a classifier separates the feature space into subspaces each of which is labeled with a category.
The Cambridge University multimedia document retrieval demo system
Authors: Andreas Tuerk, Sue E. Johnson, Pierre Jourlin, Karen Sparck Jones, Philip C. Woodland
Venue: SIGIR
Year: 2000

The CU-MDR Demo [3] is a web based application that allows the user to query a database of automatically generated transcripts of radio broadcasts that are available on-line. The system downloads the audio track of British and American news broadcasts from the Internet once a day and adds them to its archive. The audio, which comes in RealAudio format, is first converted into standard uncompressed format from which a transcription is produced using our large vocabulary broadcast news recognition engine. This yields a collection of text and audio documents which can be searched by the user. The recogniser is similar to the system running in 10 times real time described in [2]. This system gives a word error rate of 15.9% on the 1998 Hub4 broadcast news evaluation data. On the Internet audio used here the run-time is increased due to reduced audio quality but the general level of transcription accuracy remains high at approximately 20% word error rate on NPR data. The information retrieval engine used in the CU-MDR demo is the benchmark system described in [1]. A win-dowing/recombination system is used for audio data for which story boundary information is not available. Semantic posets are not automatically applied in searching. Instead they are exploited to suggest new words to the user for addition to the original query. Interactive relevance feedback is also available. This allows the user to mark the documents that contain relevant information and have the system suggest additional query words that distinguish those from the non-relevant documents. The user can either query the audio/text database interactively , submitting a request by typing a query into the search field or he/she can specify a set of standing queries that represent his/her long-term interests. The retrieval engine is run on these queries only on login. If Permmslon to make digital or hard copies of all or part of this work for personal or classroom use is granted wlthot/t fee provided that copies are not made or distributed for profit or commercial advantage and that copras bear thin notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redmtribute to listS, requires prior specific permission and/or a fee. the retriever finds a document that matches one of these queries and which has not been seen by the user before, the query is highlighted. This feature effectively allows the …
Effects of out of vocabulary words in spoken document retrieval
Authors: Philip C. Woodland, Sue E. Johnson, Pierre Jourlin, Karen Sparck Jones
Venue: SIGIR
Year: 2000

The effects of out-of-vocabulary (OOV) items in spoken document retrieval (SDR) are investigated. Several sets of transcriptions were created for the TREC-8 SDR task using a speech recognition system varying the vocabulary sizes and OOV rates, and the relative retrieval performance measured. The effects of OOV terms on a simple baseline IR system and on more sophisticated retrieval systems are described. The use of a parallel corpus for query and document expansion is found to be especially beneficial, and with this data set, good retrieval performance can be achieved even for fairly high OOV rates.
The role of a judge in a user based retrieval experiment
Authors: Mingfang Wu, Michael Fuller, Ross Wilkinson
Venue: SIGIR
Year: 2000

Article Free Access Share on The role of a judge in a user based retrieval experiment (poster session) Authors: Mingfang Wu CSIRO Mathematical and Information Science and Department of Computer Science, RMIT CSIRO Mathematical and Information Science and Department of Computer Science, RMITView Profile , Michael Fuller Department of Computer Science, RMIT Department of Computer Science, RMITView Profile , Ross Wilkinson CSIRO Mathematical and Information Science CSIRO Mathematical and Information ScienceView Profile Authors Info & Claims SIGIR '00: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrievalJuly 2000Pages 331–333https://doi.org/10.1145/345508.345628Published:01 July 2000Publication History 1citation19DownloadsMetricsTotal Citations1Total Downloads19Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Exploration of a heuristic approach to threshold learning in adaptive filtering
Authors: ChengXiang Zhai, Peter Jansen, David A. Evans
Venue: SIGIR
Year: 2000

In this paper we examine the learning behavior of a heuristic threshold setting approach to information filtering. In particular, we study how different initial threshold settings and different updating parameter settings affect threshold learning. The results on one of the TREC news databases indicate that (1) learning allows recovery from the inevitable non-optimality of the initial conditions, and (2) a greater “willingness to learn” (expressed by a deliberate lowering of the score threshold in the learning stage) does eventually lead to a higher performance in spite of the expected initial performance penalty.
Searching the Deep Web - Distributed Explorit Directed Query Applications
Authors: Valerie S. Allen, Abe Lederman
Venue: SIGIR
Year: 2001

In 1999 a directed query distributed search engine was integrated into a new Department of Energy Virtual Library of Energy Science and Technology. Millions of pages of government information across multiple agencies were made immediately searchable via one query, setting the stage for the development of a variety of interagency initiatives and applications.
Iterative Residual Rescaling: An Analysis and Generalization of LSI
Authors: Rie Kubota Ando, Lillian Lee
Venue: SIGIR
Year: 2001

We consider the problem of creating document representations in which inter-document similarity measurements correspond to semantic similarity. We first present a novelsubspace-basedframework for formalizing this task. Using this framework, we derive a new analysis ofLatent Semantic Indexing(LSI), showing a precise relationship between its performance and theuniformityof the underlying distribution of documents over topics. This analysis helps explain the improvements gained by Ando's (2000)Iterative Residual Rescaling(\ours) algorithm: \ours\ can compensate for distributional non-uniformity. A further benefit of our framework is that it provides a well-motivated, effective method for automatically determining the rescaling factor \ours\ depends on, leading to further improvements. A series of experiments over various settings and with several evaluation metrics validates our claims.
The Score-Distributional Threshold Optimization for Adaptive Binary Classification Tasks
Authors: Avi Arampatzis, André van Hameren
Venue: SIGIR
Year: 2001

The thresholding of document scores has proved critical for the effectiveness of classification tasks. We review the most important approaches to thresholding, and introduce thescore-distributional (S-D) threshold optimizationmethod. The method is based on score distributions and is capable of optimizing any effectiveness measure defined in terms of the traditional contingency table.
Models for Metasearch
Authors: Javed A. Aslam, Mark H. Montague
Venue: SIGIR
Year: 2001

Given the ranked lists of documents returned by multiple search engines in response to a given query, the problem ofmetasearchis to combine these lists in a way which optimizes the performance of the combination. This paper makes three contributions to the problem of metasearch: (1) We describe and investigate a metasearch model based on an optimal democratic voting procedure, the Borda Count; (2) we describe and investigate a metasearch model based on Bayesian inference; and (3) we describe and investigate a model for obtaining upper bounds on the performance of metasearch algorithms. Our experimental results show that metasearch algorithms based on the Borda and Bayesian models usually outperform the best input system and are competitive with, and often outperform, existing metasearch strategies. Finally, our initial upper bounds demonstrate that there is much to learn about the limits of the performance of metasearch.
Metasearch Consistency
Authors: Javed A. Aslam, Mark H. Montague
Venue: SIGIR
Year: 2001

We investigate the performance of metasearch algorithms in terms of how much they improve consistency. We find that three different metasearch algorithms, each over three datasets, usually improve the consistency of search results; sometimes the improvement is dramatic. Furthermore, consistency tends to improve when performance improves.
On Feature Distributional Clustering for Text Categorization
Authors: Ron Bekkerman, Ran El-Yaniv, Yoad Winter, Naftali Tishby
Venue: SIGIR
Year: 2001

We describe a text categorization approach that is based on a combination of feature distributional clusters with a support vector machine (SVM) classifier. Our feature selection approach employs distributional clustering of words via the recently introducedinformation bottleneck method, which generates a more efficientword-clusterrepresentation of documents. Combined with the classification power of an SVM, this method yields high performance text categorization that can outperform other recent methods in terms of categorization accuracy and representation efficiency. Comparing the accuracy of our method with other techniques, we observe significant dependency of the results on the data set. We discuss the potential reasons for this dependency.
Cite Me, Cite My References? (Scholarly Use of the ACM SIGIR Proceedings Based on Two Citation Indexes)
Authors: Elana Broch
Venue: SIGIR
Year: 2001

A three-part study was designed to document Internet use in scholarly research, using the Annual SIGIR Conference Proceedings from 1997 through 1999. The results suggest an increasing trend toward electronic self-publishing. Furthermore, while electronic availability did not insure that one would be cited, the most highly cited articles were available on the "free" web. The study also found that electronic availability has not, in most cases, decreased the length of time between publication and citation.
Enhanced Topic Distillation Using Text, Markup Tags, and Hyperlinks
Authors: Soumen Chakrabarti, Mukul Joshi, Vivek Tawde
Venue: SIGIR
Year: 2001

Topic distillation is the analysis of hyperlink graph structure to identify mutually reinforcing authorities (popular pages) and hubs (comprehensive lists of links to authorities). Topic distillation is becoming common in Web search engines, but the best-known algorithms model the Web graph at a coarse grain, with whole pages as single nodes. Such models may lose vital details in the markup tag structure of the pages, and thus lead to a tightly linked irrelevant subgraph winning over a relatively sparse relevant subgraph, a phenomenon called topic drift or contamination. The problem gets especially severe in the face of increasingly complex pages with navigation panels and advertisement links. We present an enhanced topic distillation algorithm which analyzes text, the markup tag trees that constitute HTML pages, and hyperlinks between pages. It thereby identifies subtrees which have high text- and hyperlink-based coherence w.r.t. the query. These subtrees get preferential treatment in the mutual reinforcement process. Using over 50 queries, 28 from earlier topic distillation work, we analyzed over 700,000 pages and obtained quantitative and anecdotal evidence that the new algorithm reduces topic drift.