OpenRaiser/Intern-Atlas huggingface dataset:
Browsing in a Digital Library Collecting Linearly Arranged Documents
Authors: Makoto Nakashima, Yanhua Qu, Keizo Sato, Tetsuro Ito
Venue: SIGIR
Year: 2001

A method of assisting a user in finding the required documents effectively is proposed. A user being informed which documents are worth examining can browse in a digital library (DL) in a linear fashion. Computational evaluations were carried out, and a DL and its navigator are designed and constructed.
A Method Based on the Chi-Square Test for Document Classification
Authors: Michael P. Oakes, Robert J. Gaizauskas, Helene Fowkes
Venue: SIGIR
Year: 2001

We introduce a method for document classification based on using the chi-square test to identify characteristic vocabulary of document classes.
Structure and Content-Based Segmentation of Speech Transcripts
Authors: Dulce B. Ponceleon, Savitha Srinivasan
Venue: SIGIR
Year: 2001

algorithm for the segmentation of an audio/video source into topically cohesive segments based on automatic speech recognition (ASR) transcriptions is presented. A novel two-pass algorithm is described that combines a boundary-based method with a content-based method. In the first pass, the temporal proximity and the rate of arrival of ngram features is analyzed in order to compute an initial segmentation. In the content- based second pass, changes in content-bearing words are detected by using the ngram features as queries in an information-retrieval system. The second pass validates the initial segments and merges them as needed. Feasibility of the segmentation task can vary enormously depending on the structure of the audio content, and the accuracy of ASR. For real-world corporate training data our method identifies, at worst, a single salient segment of the audio and, at best, a high-level table-of-contents. We illustrate the algorithm in detail with some examples and validate the results with segmentation boundaries generated manually.
Empirical Investigations on Query Modification Using Abductive Explanations
Authors: Ian Ruthven, C. J. van Rijsbergen, Mounia Lalmas
Venue: SIGIR
Year: 2001

In this paper we report on a series of experiments designed to investigate query modification techniques motivated by the area of abductive reasoning. In particular we use the notion of abductive explanation, explanations being a description of data that highlight important features of the data. We describe several methods of creating abductive explanations, exploring term reweighting and query reformulation techniques and demonstrate their suitability for relevance feedback.
Rank-Preserving Two-Level Caching for Scalable Search Engines
Authors: Patricia Correia Saraiva, Edleno Silva de Moura, Rodrigo C. Fonseca, Wagner Meira Jr., Berthier A. Ribeiro-Neto, Nivio Ziviani
Venue: SIGIR
Year: 2001

Article Rank-preserving two-level caching for scalable search engines Share on Authors: Patricia Correia Saraiva Federal Univ. of Minas Gerais, Belo Horizonte, Brazil and Federal Univ. of Amazonas, Manaus, Brazil Federal Univ. of Minas Gerais, Belo Horizonte, Brazil and Federal Univ. of Amazonas, Manaus, BrazilView Profile , Edleno Silva de Moura Akwan Information Technologies, Belo Horizonte, Brazil Akwan Information Technologies, Belo Horizonte, BrazilView Profile , Nivio Ziviani Federal Univ. of Minas Gerais, Belo Horizonte, Brazil Federal Univ. of Minas Gerais, Belo Horizonte, BrazilView Profile , Wagner Meira Federal Univ. of Minas Gerais, Belo Horizonte, Brazil Federal Univ. of Minas Gerais, Belo Horizonte, BrazilView Profile , Rodrigo Fonseca Univ. of Minas, Belo Horizonte, Brazil Univ. of Minas, Belo Horizonte, BrazilView Profile , Berthier Ribeiro-Neto Federal Univ. of Minas Gerias, Belo Horizonte, Brazil Federal Univ. of Minas Gerias, Belo Horizonte, BrazilView Profile Authors Info & Claims SIGIR '01: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrievalSeptember 2001 Pages 51–58https://doi.org/10.1145/383952.383959Published:01 September 2001 89citation981DownloadsMetricsTotal Citations89Total Downloads981Last 12 Months8Last 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
Discovering Information Flow Using a High Dimensional Conceptual Space
Authors: Dawei Song, Peter Bruza
Venue: SIGIR
Year: 2001

This paper presents an informational inference mechanism realized via the use of a high dimensional conceptual space. More specifically, we claim to have operationalized important aspects of G rdenforss recent three-level cognitive model. The connectionist level is primed with the Hyperspace Analogue to Language (HAL) algorithm which produces vector representations for use at the conceptual level. We show how inference at the symbolic level can be implemented by employing Barwise and Seligmans theory of information flow. This article also features heuristics for enhancing HAL-based representations via the use of quality properties, determining concept inclusion and computing concept composition. The worth of these heuristics in underpinning informational inference are demonstrated via a series of experiments. These experiments, though small in scale, show that informational inference proposed in this article has a very different character to the semantic associations produced by the Minkowski distance metric and concept similarity computed via the cosine coefficient. In short, informational inference generally uncovers concepts that are carried, or, in some cases, implied by another concept, (or combination of concepts).
Using Event Segmentation to Improve Indexing of Consumer Photographs
Authors: Amanda Stent, Alexander C. Loui
Venue: SIGIR
Year: 2001

Automatic albuming --- the automatic organization of photographs, either as an end in itself or for use in other applications -- is an application that promises to be of great assistance to photographers. Relatively sophisticated image content analysis techniques have been used for image indexing, organization and retrieval. In this paper, we describe a method of organizing photographs into events using spoken photograph captions. The results of this process can be used to improve image indexing and retrieval.
Combining Semantic and Syntactic Document Classifiers to Improve First Story Detection
Authors: Nicola Stokes, Joe Carthy
Venue: SIGIR
Year: 2001

In this paper we describe a type of data fusion involving the combination of evidence derived from multiple document representations. Our aim is to investigate if a composite representation can improve the online detection of novel events in a stream of broadcast news stories. This classification process otherwise known as first story detection FSD (or in the Topic Detection and Tracking pilot study as online new event detection [1]), is one of three main classification tasks defined by the TDT initiative. Our composite document representation consists of a semantic representation (based on the lexical chains derived from a text) and a syntactic representation (using proper nouns). Using the TDT1 evaluation methodology, we evaluate a number of document representation combinations using these document classifiers.
Query-Based Web Page Summarisation: A Task-Oriented Evaluation
Authors: Ryen White, Joemon M. Jose, Ian Ruthven
Venue: SIGIR
Year: 2001

We present a system that offers a new way of assessing web document relevance and new approach to the web-based evaluation of such a system. Provisionally named WebDocSum, the system is a query-biased web page summariser that aims to provide an alternative to the short, irrelevant abstracts typical of many web search result lists. Based on an initial evaluation the system appears to be more useful in helping users gauge document relevance than the traditional ranked titles/abstracts approach.
Searcher Performance in Question Answering
Authors: Ross Wilkinson, Mingfang Wu, Michael Fuller
Venue: SIGIR
Year: 2001

There are many tasks that require information finding. Some can be largely automated, and others greatly benefit from successful interaction between system and searcher. We are interested in the task of answering questions where some synthesis of information is required-the answer would not generally be given from a single passage of a single document. We investigate whether variation in the way a list of documents is delivered affected searcher performance in the question answering task. We will show that there is a significant difference in performance using a list customized to the task type, compared with a standard web-engine list. This indicates that paying attention to the task and the searcher interaction may provide substantial improvement in task performance.
Automatic Information Extraction from Web Pages
Authors: Roland H. C. Yap, Budi Rahardjo
Venue: SIGIR
Year: 2001

Many web pages have implicit structure. In this paper, we show the feasibility of automatically extracting data from web pages by using approximate matching techniques. This can be applied to generate automatic wrappers or to notify/display web page differences, web page change monitoring, etc.
Maximum Likelihood Estimation for Filtering Thresholds
Authors: Yi Zhang, James P. Callan
Venue: SIGIR
Year: 2001

Information filtering systems based on statistical retrieval models usually compute a numeric score indicating how well each document matches each profile. Documents with scores above profile-specificdissemination thresholdsare delivered. An optimal dissemination threshold is one that maximizes a given utility function based on the distributions of the scores of relevant and non-relevant documents. The parameters of the distribution can be estimated using relevance information, but relevance information obtained while filtering isbiased. This paper presents a new method of adjusting dissemination thresholds that explicitly models and compensates for this bias. The new algorithm, which is based on the Maximum Likelihood principle, jointly estimates the parameters of the density distributions for relevant and non-relevant documents and the ratio of the relevant document in the corpus. Experiments with TREC-8 and TREC-9 Filtering Track data demonstrate the effectiveness of the algorithm.
The use of unlabeled data to improve supervised learning for text summarization
Authors: Massih-Reza Amini, Patrick Gallinari
Venue: SIGIR
Year: 2002

With the huge amount of information available electronically, there is an increasing demand for automatic text summarization systems. The use of machine learning techniques for this task allows one to adapt summaries to the user needs and to the corpus characteristics. These desirable properties have motivated an increasing amount of work in this field over the last few years. Most approaches attempt to generate summaries by extracting sentence segments and adopt the supervised learning paradigm which requires to label documents at the text span level. This is a costly process, which puts strong limitations on the applicability of these methods. We investigate here the use of semi-supervised algorithms for summarization. These techniques make use of few labeled data together with a larger amount of unlabeled data. We propose new semi-supervised algorithms for training classification models for text summarization. We analyze their performances on two data sets - the Reuters news-wire corpus and the Computation and Language (cmp_lg) collection of TIPSTER SUMMAC. We perform comparisons with a baseline - non learning - system, and a reference trainable summarizer system.
Impact transformation: effective and efficient web retrieval
Authors: Vo Ngoc Anh, Alistair Moffat
Venue: SIGIR
Year: 2002

We extend the applicability of impact transformation, which is a technique for adjusting the term weights assigned to documents so as to boost the effectiveness of retrieval when short queries are applied to large document collections. In conjunction with techniques called quantization and thresholding, impact transformation allows improved query execution rates compared to traditional vector-space similarity computations, as the number of arithmetic operations can be reduced. The transformation also facilitates a new dynamic query pruning heuristic. We give results based upon the trec web data that show the combination of these various techniques to yield highly competitive retrieval, in terms of both effectiveness and efficiency, for both short and long queries.
Efficient phrase querying with an auxiliary index
Authors: Dirk Bahle, Hugh E. Williams, Justin Zobel
Venue: SIGIR
Year: 2002

Search engines need to evaluate queries extremely fast, a challenging task given the vast quantities of data being indexed. A significant proportion of the queries posed to search engines involve phrases. In this paper we consider how phrase queries can be efficiently supported with low disk overheads. Previous research has shown that phrase queries can be rapidly evaluated using nextword indexes, but these indexes are twice as large as conventional inverted files. We propose a combination of nextword indexes with inverted files as a solution to this problem. Our experiments show that combined use of an auxiliary nextword index and a conventional inverted file allow evaluation of phrase queries in half the time required to evaluate such queries with an inverted file alone, and the space overhead is only 10% of the size of the inverted file. Further time savings are available with only slight increases in disk requirements.
Probabilistic combination of text classifiers using reliability indicators: models and results
Authors: Paul N. Bennett, Susan T. Dumais, Eric Horvitz
Venue: SIGIR
Year: 2002

The intuition that different text classifiers behave in qualitatively different ways has long motivated attempts to build a better metaclassifier via some combination of classifiers. We introduce a probabilistic method for combining classifiers that considers the context-sensitive reliabilities of contributing classifiers. The method harnesses reliability indicators---variables that provide a valuable signal about the performance of classifiers in different situations. We provide background, present procedures for building metaclassifiers that take into consideration both reliability indicators and classifier outputs, and review a set of comparative studies undertaken to evaluate the methodology.
ICA and SOM in text document analysis
Authors: Ella Bingham, Jukka Kuusisto, Krista Lagus
Venue: SIGIR
Year: 2002

In this study we show experimental results on using Independent Component Analysis (ICA) and the Self-Organizing Map (SOM) in document analysis. Our documents are segments of spoken dialogues carried out over the telephone in a customer service, transcribed into text. The task is to analyze the topics of the discussions, and to group the discussions into meaningful subsets. The quality of the grouping is studied by comparing to a manual topical classification of the documents.
Automatic query refinement using lexical affinities with maximal information gain
Authors: David Carmel, Eitan Farchi, Yael Petruschka, Aya Soffer
Venue: SIGIR
Year: 2002

This work describes an automatic query refinement technique, which focuses on improving precision of the top ranked documents. The terms used for refinement are lexical affinities (LAs), pairs of closely related words which contain exactly one of the original query terms. Adding these terms to the query is equivalent to re-ranking search results, thus, precision is improved while recall is preserved. We describe a novel method that selects the most informative LAs for refinement, namely, those LAs that best separate relevant documents from irrelevant documents in the set of results. The information gain of candidate LAs is determined using unsupervised estimation that is based on the scoring function of the search engine. This method is thus fully automatic and its quality depends on the quality of the scoring function. Experiments we conducted with TREC data clearly show a significant improvement in the precision of the top ranked documents.
Bayesian online classifiers for text classification and filtering
Authors: Kian Ming Adam Chai, Hai Leong Chieu, Hwee Tou Ng
Venue: SIGIR
Year: 2002

This paper explores the use of Bayesian online classifiers to classify text documents. Empirical results indicate that these classifiers are comparable with the best text classification systems. Furthermore, the online approach offers the advantage of continuous learning in the batch-adaptive text filtering task.
Predicting category accesses for a user in a structured information space
Authors: Mao Chen, Andrea S. LaPaugh, Jaswinder Pal Singh
Venue: SIGIR
Year: 2002

In a categorized information space, predicting users ’ information needs at the category level can facilitate personalization, caching and other topic-oriented services. This paper presents a two-phase model to predict the category of a user’s next access based on previous accesses. Phase 1 generates a snapshot of a user’s preferences among categories based on a temporal and frequency analysis of the user’s access history. Phase 2 uses the computed preferences to make predictions at different category granularities. Several alternatives for each phase are evaluated, using the rating behaviors of on-line raters as the form of access considered. The results show that a method based on re-access pattern and frequency analysis of a user’s whole history has the best prediction quality, even over a path-based method (Markov model) that uses the combined history of all users.
Document normalization revisited
Authors: Abdur Chowdhury, M. Catherine McCabe, David A. Grossman, Ophir Frieder
Venue: SIGIR
Year: 2002

Cosine Pivoted Document Length Normalization has reached a point of stability where many researchers indiscriminately apply a specific value of 0.2 regardless of the collection. Our efforts, however, demonstrate that applying this specific value without tuning for the document collection degrades average precision by as much as 20%.
Automatic evaluation of world wide web search services
Authors: Abdur Chowdhury, Ian Soboroff
Venue: SIGIR
Year: 2002

Users of the World-Wide Web are not only confronted by an immense overabundance of information, but also by a plethora of tools for searching for the web pages that suit their information needs. Web search engines differ widely in interface, features, coverage of the web, ranking methods, delivery of advertising, and more. In this paper, we present a method for comparing search engines automatically based on how they rank known item search results. Because the engines perform their search on overlapping (but different) subsets of the web collected at different points in time, evaluation of search engines poses significant challenges to the traditional information retrieval methodology. Our method uses known item searching; comparing the relative ranks of the items in the search engines' rankings. Our approach automatically constructs known item queries using query log analysis and automatically constructs the result via analysis of editor comments from the ODP (Open Directory Project). Additionally, we present our comparison on five (Lycos, Netscape, Fast, Google, HotBot) well-known search services and find that some services perform known item searches better than others, but the majority are statistically equivalent.
Adaptive information extraction for document annotation in amilcare
Authors: Fabio Ciravegna, Alexiei Dingli, Yorick Wilks, Daniela Petrelli
Venue: SIGIR
Year: 2002

Amilcare is a tool for Adaptive Information Extraction (IE) designed for supporting active annotation of documents for the Semantic Web (SW). It can be used either for unsupervised document annotation or as a support for human annotation. Amilcare is portable to new applications/domains without any knowledge of IE, as it just requires users to annotate a small training corpus with the information to be extracted. It is based on (LP)2, a supervised learning strategy for IE able to cope with different texts types, from newspaper-like texts, to rigidly formatted Web pages and even a mixture of them[1][5].Adaptation starts with the definition of a tag set for annotation, possibly organized as an ontology. Then users have to manually annotate a small training corpus. Amilcare provides a default mouse-based interface called Melita, where annotations are inserted by first selecting a tag from the ontology and then identifying the text area to annotate with the mouse. Differently from similar annotation tools [4, 5], Melita actively supports training corpus annotation. While users annotate texts, Amilcare runs in the background learning how to reproduce the inserted annotation. Induced rules are silently applied to new texts and their results are compared with the user annotation. When its rules reach a (user-defined) level of accuracy, Melita presents new texts with a preliminary annotation derived by the rule application. In this case users have just to correct mistakes and add missing annotations. User corrections are inputted back to the learner for retraining. This technique focuses the slow and expensive user activity on uncovered cases, avoiding requiring annotating cases where a satisfying effectiveness is already reached. Moreover validating extracted information is a much simpler task than tagging bare texts (and also less error prone), speeding up the process considerably. At the end of the corpus annotation process, the system is trained and the application can be delivered. MnM [6] and Ontomat annotizer [7] are two annotation tools adopting Amilcare's learner.In this demo we simulate the annotation of a small corpus and we show how and when Amilcare is able to support users in the annotation process, focusing on the way the user can control the tool's proactivity and intrusivity. We will also quantify such support with data derived from a number of experiments on corpora. We will focus on training corpus size and correctness of suggestions when the corpus is increased.
Effective collection metasearch in a hierarchical environment: global vs. localized retrieval performance
Authors: Jack G. Conrad, Changwen Yang, Joanne S. Claussen
Venue: SIGIR
Year: 2002

We compare standard global IR searching with user-centric localized techniques to address the database selection problem. We conduct a series of experiments to compare the retrieval effectiveness of three separate search modes applied to a hierarchically structured data environment of textual database representations. The data environment is represented as a tree-like directory containing over 15,000 unique databases and over 100,000 total leaf nodes. Our search modes consist of varying degrees of browse and search, from a global search at the root node to a refined search at a sub-node using dynamically-calculated inverse document frequencies (idfs) to score candidate databases for probable relevance. Our findings indicate that a browse and search approach that relies upon localized searching from sub-nodes is capable of producing the most effective results.
Inverted file search algorithms for collaborative filtering
Authors: Rickard Cöster, Martin Svensson
Venue: SIGIR
Year: 2002

This paper explores the possibility of using a disk based inverted file structure for collaborative filtering. Our hypothesis is that this allows for faster calculation of predictions and also that early termination heuristics may be used to further speed up the filtering process and perhaps even improve the quality of the predictions. In an experiment on the EachMovie dataset this was tested. Our results indicate that searching the inverted file structure is many times faster than general in-memory vector search, even for very large profiles. The Continue termination heuristics produces the best ranked predictions in our experiments, and Quit is the top performer in terms of speed.