OpenRaiser/Intern-Atlas huggingface dataset:
The Decomposition of Human-Written Summary Sentences
Authors: Hongyan Jing, Kathleen R. McKeown
Venue: SIGIR
Year: 1999

We define the problem of decomposing human-written summary sentences and propose a novel Hidden Markov Model solution to the problem. Human summarizers often rely on cutting and pasting of the full document to generate summaries. Decomposing a human-written summary sentence requires determining: (1) whether it is constructed by cutting and pasting, ( Solving the decomposition problem can potentially lead to the automatic acquisition of large corpora for summarization. It also sheds light on the generation of summary text by cutting and pasting. The evaluation shows that the proposed decomposition algorithm performs well.
Information Retrieval Based on Context Distance and Morphology
Authors: Hongyan Jing, Evelyne Tzoukermann
Venue: SIGIR
Year: 1999

We present an approach to information retrieval based on context distance and morphology. Context distance is a measure we use to assess the closeness of word meanings. This context distance model measures semantic distances between words using the local contexts of words within a single document as well as the lexical co-occurrence information in the set of documents to be retrieved. We also propose to integrate the context distance model with morphological analysis in determining word similarity so that the two can enhance each other. Using the standard vector-space model, we evaluated the proposed method on a subset of TREC-4 corpus (AP88 and AP90 collection, 158,240 documents, 49 queries). Results show that this method improves the 11-point average precision by 8.6%.
Effective Document Presentation with a Locality-Based Similarity Heuristic
Authors: Owen de Kretser, Alistair Moffat
Venue: SIGIR
Year: 1999

Article Free Access Share on Effective document presentation with a locality-based similarity heuristic Authors: Owen de Kretser Department of Computer Science and Software Engineering, The University of Melbourne, Parkville, Victoria 3052, Australia Department of Computer Science and Software Engineering, The University of Melbourne, Parkville, Victoria 3052, AustraliaView Profile , Alistair Moffat Department of Computer Science and Software Engineering, The University of Melbourne, Parkville, Victoria 3052, Australia Department of Computer Science and Software Engineering, The University of Melbourne, Parkville, Victoria 3052, AustraliaView Profile Authors Info & Claims SIGIR '99: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrievalAugust 1999Pages 113–120https://doi.org/10.1145/312624.312664Published:01 August 1999Publication History 46citation732DownloadsMetricsTotal Citations46Total Downloads732Last 12 Months31Last 6 weeks10 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
Context-Sensitive Vocabulary Mapping with a Spreading Activation Network
Authors: Jonghoon Lee, David Dubin
Venue: SIGIR
Year: 1999

Article Context-sensitive vocabulary mapping with a spreading activation network Share on Authors: Jonghoon Lee Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign Graduate School of Library and Information Science, University of Illinois at Urbana-ChampaignView Profile , David Dubin Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign Graduate School of Library and Information Science, University of Illinois at Urbana-ChampaignView Profile Authors Info & Claims SIGIR '99: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrievalAugust 1999 Pages 198–205https://doi.org/10.1145/312624.312678Published:01 August 1999 3citation763DownloadsMetricsTotal Citations3Total Downloads763Last 12 Months4Last 6 weeks2 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 SiteGet Access
Using a Belief Revision Operator for Document Ranking in Extended Boolean Models
Authors: David E. Losada, Alvaro Barreiro
Venue: SIGIR
Year: 1999

This paper claims that Belief Revision can be seen as a theoretical framework for document ranking in Extended Boolean Models. For a model of Information Retrieval based on propositional logic, we propose a similarity measure which is equivalent to a P-Norm case. Therefore it shares the P-Norm good properties and behaviour. Besides, it is theoretically ensured that this measure follows the notion of proximity b e t ween the documents and the query. The logical model can naturally deal with incomplete descriptions of documents and the similarity v alues are also obtained for this case.
Partial Replica Selection Based on Relevance for Information Retrieval
Authors: Zhihong Lu, Kathryn S. McKinley
Venue: SIGIR
Year: 1999

Partial collection replication improves performance and scalability of a large-scale distributed information retrieval system by distributing excessive w orkloads, reducing network latency, and restricting some searches to a small percentage of data. In this paper, we rst examine queries from real system logs and show that there is su cient query locality in real systems to justify partial collection replication. We then present a method for constructing a hierarchy of partial replicas from a collection where each replica is a subset of all larger replicas, and extend the inference network model to rank and select partial replicas. We compare our new selection algorithm to previous work on collection selection over a range of tuning parameters. For a given query, our replica selection algorithm correctly determines the most relevant of the replicas or original collection, and thus maintains the highest retrieval e ectiveness while searching the least data as compared with the other ranking functions. Simulation results show that with load balancing, partial replication consistently improves performance over collection partitioning on multiple disks of a shared-memory multiprocessor and it requires only modest query locality.
The Automatic Construction of Large-Scale Corpora for Summarization Research
Authors: Daniel Marcu
Venue: SIGIR
Year: 1999

Summarization research is notorious for its lack of adequate corpora: today, there exist only a few small collections of texts whose units have been manually annotated for textual importance. Given the cost and tediousness of the annotation process, it is very unlikely that we will ever manually annotate for textual importance sufficiently large corpora of texts. To circumvent this problem, we have developed an algorithm that constructs such corpora automatically. Our algorithm takes as input an hAbstract, Texti tuple and generates the corresponding Extract, i.e., the set of clauses (sentences) in the Text that were used to write the Abstract. The performance of the algorithm is shown to be close to that of humans by means of an empirical experiment. The experiment also suggests extraction strategies that could improve the performance of automatic summarization systems. 1 Introduction 1.1 Motivation All research on the automatic generation of generic abstracts assumes that the firs...
Cross-Language Information Retrieval Based on Parallel Texts and Automatic Mining of Parallel Texts from the Web
Authors: Jian-Yun Nie, Michel Simard, Pierre Isabelle, Richard Durand
Venue: SIGIR
Year: 1999

This paper describes the use of a probabilistic translation model to cross-language IR (CLIR). The performance of this approach is compared with that using machine translation (MT). It is shown that using a probabilistic model, we are able to obtain performances close to those using an MT system. In addition, we also investigated the possibility of automatically gather parallel texts from the Web in an attempt to construct a reasonable training corpus. The result is very encouraging. We showed that in several tests, such a training corpus is as good as a manually constructed one for CLIR purposes.
"User Revealment" - a Comparison of Initial Queries and Ensuing Question Development in Online Searching and in Human Reference Interactions
Authors: Ragnar Nordlie
Venue: SIGIR
Year: 1999

Article "User revealment"—a comparison of initial queries and ensuing question development in online searching and in human reference interactions Share on Author: Ragnar Nordlie Oslo College, Faculty of Journalism, Library and Information Studies, Pilestredet 52, 0167 Oslo, Norway Oslo College, Faculty of Journalism, Library and Information Studies, Pilestredet 52, 0167 Oslo, NorwayView Profile Authors Info & Claims SIGIR '99: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrievalAugust 1999 Pages 11–18https://doi.org/10.1145/312624.312618Published:01 August 1999 43citation1,015DownloadsMetricsTotal Citations43Total Downloads1,015Last 12 Months15Last 6 weeks3 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 SiteGet Access
Content-Based Retrieval Using Heuristic Search
Authors: Dimitris Papadias, Marios Mantzourogiannis, Panos Kalnis, Nikos Mamoulis, Ishfaq Ahmad
Venue: SIGIR
Year: 1999

The fast growth of multimedia information in image and video databases has triggered research on efficient retrieval methods. This paper deals with structural queries, a type of content-based retrieval where similarity is not defined on visual properties such as color and texture, but on object relations in space. We propose the application of heuristic algorithms which provide good, but not necessarily optimal, solutions in a pre-determined time period, and compare our approach with systematic search methods which are guaranteed to find optimal solutions but require exponential time in the worst case. The quality of the output is calculated using a relation framework which is an extension of Allen's relations. With this framework our methods can be applied in multiple resolutions and dimensions, thus covering a wide range of applications in spatial, multimedia and video systems.
Efficient Distributed Algorithms to Build Inverted Files
Authors: Berthier A. Ribeiro-Neto, Edleno Silva de Moura, Marden S. Neubert, Nivio Ziviani
Venue: SIGIR
Year: 1999

We present three distributed algorithms to build global inverted files for very large text collections. The distributed environment we use is a high bandwidth network of workstations with a shared-nothing memory organization.
Deriving Concept Hierarchies from Text
Authors: Mark Sanderson, W. Bruce Croft
Venue: SIGIR
Year: 1999

This paper presents a means of automatically deriving a hierarchical organization of concepts from a set of documents without use of training data or standard clustering techniques. Instead, salient words and phrases extracted from the documents are organized hierarchically using a type of co-occurrence known as subsumption. The resulting structure is displayed as a series of hierarchical menus. When generated from a set of retrieved documents, a user browsing the menus is provided with a detailed overview of their content in a manner distinct from existing overview and summarization techniques. The methods used to build the structure are simple, but appear to be effective: a smallscale user study reveals that the generated hierarchy possesses properties expected of such a structure in that general terms are placed at the top levels leading to related and more specific terms below. The formation and presentation of the hierarchy is described along with the user study and some other informal evaluations.
Content-Based Retrieval for Music Collections
Authors: Yuen-Hsien Tseng
Venue: SIGIR
Year: 1999

A content-based retrieval model for tackling the mismatch problems specific to music data is proposed and implemented. The system uses a pitch profile encoding for queries in any key and an n-note indexing method for approximate matching in sub-linear time. A distinct function that extracts key melodies for query suggestion is developed. The Web-based system provides flexible user interface for query formulation and result browsing. Users can search the system by a short sequence of notes, by uploading a file created by singing, or by clicking suggested key melodies without input. Experiments show that the pitch profile encoding and a 3-note indexing are able to overcome the key mismatch problem and the random errors caused by pitch error, note deletion and insertion. The use of extracted key melodies improves performance over direct search of the music database. For the type of burst mismatch, a query expansion approach is applied.
Ultra-Summarization: A Statistical Approach to Generating Highly Condensed Non-Extractive Summaries (poster abstract)
Authors: Michael J. Witbrock, Vibhu O. Mittal
Venue: SIGIR
Year: 1999

Abstract Using current extractive summarization techniques, it is impossible to produce a coherent document summary shorter than a single sentence, or to produce a summary that conforms to particular stylistic constraints. Ideally, one would prefer to understand the document, and to generate an appropriate summary directly from the results of that understanding. Absent a comprehensive natural language understanding system, an approximation must be used. This paper presents an alternative statistical model of a summarization process, which jointly applies statistical models of the term selection and term ordering process to produce brief coherent summaries in a style learned from a training corpus.
Cluster-Based Language Models for Distributed Retrieval
Authors: Jinxi Xu, W. Bruce Croft
Venue: SIGIR
Year: 1999

Effective retrieval in a distributed environment is an important but difficult problem. Lack of effectiveness appears to have three causes. First, collection selection based on word histograms is not appropriate for heterogeneous collections. Second, relevant documents are scattered over many collections and searching a few collections misses many relevant documents. Third, most existing collection selection metrics lack sound theoretical justifications and hence may not be well tuned to the problem. We propose a new approach to distributed retrieval based on document clustering and language modeling. Document clustering is used to organize collections around topics. Language modeling is used to properly represent topics and effectively select the right topics for a query. Based on these ideas, three methods are proposed to suit different environments. We show that all three methods improve effectiveness of distributed retrieval. 1 Introduction Information has become highly distribut...
The feature quantity: an information theoretic perspective of Tfidf-like measure
Authors: Akiko N. Aizawa
Venue: SIGIR
Year: 2000

The feature quantity, a quantitative representation of specificity introduced in this paper, is based on an information theoretic perspective of co-occurrence events between terms and documents. Mathematically, the feature quantity is defined as a product of probability and information, and maintains a good correspondence with the tfidf-like measures popularly used in today's IR systems. In this paper, we present a formal description of the feature quantity, as well as some illustrative examples of applying such a quantity to different types of information retrieval tasks: representative term selection and text categorization.
Does "authority" mean quality? predicting expert quality ratings of Web documents
Authors: Brian Amento, Loren G. Terveen, William C. Hill
Venue: SIGIR
Year: 2000

For many topics, the World Wide Web contains hundreds or thousands of relevant documents of widely varying quality. Users face a daunting challenge in identifying a small subset of documents worthy of their attention.
An experimental comparison of naive bayesian and keyword-based anti-spam filtering with personal e-mail messages
Authors: Ion Androutsopoulos, John Koutsias, Konstantinos Chandrinos, Constantine D. Spyropoulos
Venue: SIGIR
Year: 2000

The growing problem of unsolicited bulk e-mail, also known as “spam”, has generated a need for reliable anti-spam e-mail filters. Filters of this type have so far been based mostly on manually constructed keyword patterns. An alternative approach has recently been proposed, whereby a Naive Bayesian classifier is trained automatically to detect spam messages. We test this approach on a large collection of personal e-mail messages, which we make publicly available in “encrypted” form contributing towards standard benchmarks. We introduce appropriate cost-sensitive measures, investigating at the same time the effect of attribute-set size, training-corpus size, lemmatization, and stop lists, issues that have not been explored in previous experiments. Finally, the Naive Bayesian filter is compared, in terms of performance, to a filter that uses keyword patterns, and which is part of a widely used e-mail reader.
Bayes optimal metasearch: a probabilistic model for combining the results
Authors: Javed A. Aslam, Mark H. Montague
Venue: SIGIR
Year: 2000

We introduce a new, probabilistic model for combining the outputs of an arbitrary number of query retrieval systems. By gathering simple statistics on the average performance of a given set of query retrieval systems, we construct a Bayes optimal mechanism for combining the outputs of these systems. Our construction yields a metasearch strategy whose empirical performance nearly always exceeds the performance of any of the constituent systems. Our construction is also robust in the sense that if “good” and “bad” systems are combined, the Performance of the composite is still on par with, or exceeds, that of the best constituent system. Finally, our model and theory provide theoretical and empirical avenues for the improvement of this metasearch strategy.
Multiple evidence combination in image retrieval: diogenes searches for people on the Web
Authors: Y. Alp Aslandogan, Clement T. Yu
Venue: SIGIR
Year: 2000

In this work, we examine evidence combination mechanisms for classifying multimedia information. In particular, we examine linear and Dempster-Shafer methods of evidence combination in the context of identifying personal images on the World Wide Web. An automatic web search engine named Diogenes1 searches the web for personal images and combines different pieces of evidence for identification. The sources of evidence consist of input from face detection/recognition and text/HTML analysis modules. A degree of uncertainty is involved with both of these sources. Diogenes automatically determines the uncertainty locally for each retrieval and uses this information to set a relative significance for each evidence. To our knowledge, Diogenes is the first image search engine using Dempster-Shafer evidence combination based on automatic object recognition and dynamic local uncertainty assessment. In our experiments Diogenes comfortably outperformed some well known commercial and research prototype image search engines for celebrity image queries.
New paradigms in information visualization
Authors: Peter Au, Matthew Carey, Shalini Sewraz, Yike Guo, Stefan M. Rüger
Venue: SIGIR
Year: 2000

We present three new visualization front-ends that aid navigation through the set of documents returned by a search engine (hit documents). We cluster the hit documents to visually group these documents and label the groups with related words. The different front-ends cater for different user needs, but all can browse cluster information as well as drilling up or down in one or more clusters and refining the search using one or more of the suggested related keywords.
Latent semantic indexing model for boolean query formulation
Authors: Dae-Ho Baek, Heui-Seok Lim, Hae-Chang Rim
Venue: SIGIR
Year: 2000

A new model named Boolean Latent Semantic Indexing model based on the Singular Value Decomposition and Boolean query formulation is introduced. While the Singular Value Decomposition alleviates the problems of lexical matching in the traditional information retrieval model, Boolean query formulation can help users to make precise representation of their information search needs. Retrieval experiments on a number of test collections seem to show that the proposed model achieves substantial performance gains over the Latent Semantic Indexing model.
Captive approach for building user model in an information retrieval context
Authors: Amina Sayeb Belhassen, Nabil Ben Abdallah, Henda Hajjami Ben Ghézala
Venue: SIGIR
Year: 2000

The recent development of communication networks and multimedia system provides users with the availability of a huge amount of information making worse the problem of information overload [9]. The evolution of system design is necessary becoming more user centred, and more personally involving. A review of survey studies on Internet users since 1993 confirms that a greater percentage of people are becoming online citizens, and professionals are integrating more online components into their work process. A review of the experimental literature on Internet user's reveals that there is intense interest in humanising the online environment by integrating affective and cognitive components [8].
OCELOT: a system for summarizing Web pages
Authors: Adam L. Berger, Vibhu O. Mittal
Venue: SIGIR
Year: 2000

We introduce OCELOT, a prototype system for automatically generating the “gist” of a web page by summarizing it. Although most text summarization research to date has focused on the task of news articles, web pages are quite different in both structure and content. Instead of coherent text with a well-defined discourse structure, they are more often likely to be a chaotic jumble of phrases, links, graphics and formatting commands. Such text provides little foothold for extractive summarization techniques, which attempt to generate a summary of a document by excerpting a contiguous, coherent span of text from it. This paper builds upon recent work in non-extractive summarization, producing the gist of a web page by “translating” it into a more concise representation rather than attempting to extract a text span verbatim. OCELOT uses probabilistic models to guide it in selecting and ordering words into a gist. This paper describes a technique for learning these models automatically from a collection of human-summarized web pages.