OpenRaiser/Intern-Atlas huggingface dataset:
Model-averaged latent semantic indexing
Authors: Miles Efron
Venue: SIGIR
Year: 2007

This poster introduces a novel approach to information retrieval that uses statistical model averaging to improve latent semantic indexing (LSI). Instead of choosing a single dimensionality $k$ for LSI , we propose using several models of differing dimensionality to inform retrieval. To manage this ensemble we weight each model's contribution to an extent inversely proportional to its AIC (Akaike information criterion). Thus each model contributes proportionally to its expected Kullback-Leibler divergence from the distribution that generated the data. We present results on three standard IR test collections, demonstrating significant improvement over both the traditional vector space model and single-model LSI.
Active learning for class imbalance problem
Authors: Seyda Ertekin, Jian Huang, C. Lee Giles
Venue: SIGIR
Year: 2007

The class imbalance problem has been known to hinder the learning performance of classification algorithms. Various real-world classification tasks such as text categorization suffer from this phenomenon. We demonstrate that active learning is capable of solving the problem.
Hierarchical classification for automatic image annotation
Authors: Jianping Fan, Yuli Gao, Hangzai Luo
Venue: SIGIR
Year: 2007

In this paper, a hierarchical classification framework has been proposed for bridging the semantic gap effectively and achieving multi-level image annotation automatically. First, the semantic gap between the low-level computable visual features and users' real information needs is partitioned into four smaller gaps, and multiple approachesallare proposed to bridge these smaller gaps more effectively. To learn more reliable contextual relationships between the atomic image concepts and the co-appearances of salient objects, a multi-modal boosting algorithm is proposed. To enable hierarchical image classification and avoid inter-level error transmission, a hierarchical boosting algorithm is proposed by incorporating concept ontology and multi-task learning to achieve hierarchical image classifier training with automatic error recovery. To bridge the gap between the computable image concepts and the users' real information needs, a novel hyperbolic visualization framework is seamlessly incorporated to enable intuitive query specification and evaluation by acquainting the users with a good global view of large-scale image collections. Our experiments on large-scale image databases have also obtained very positive results.
Document clustering: an optimization problem
Authors: Ao Feng
Venue: SIGIR
Year: 2007

Clustering algorithms have been widely used in information retrieval applications. However, it is difficult to define an objective best result. This article analyzes some document clustering algorithms and illustrates that they are equivalent to the optimization problem of some global functions. Experiments show their good performance, but there are still counter-examples where they fail to return the optimal solution. We argue that Monte-Carlo algorithms in the global optimization framework have the potential to find better solutions than traditional clustering, and they are able to handle more complex structures.
Compressed permuterm index
Authors: Paolo Ferragina, Rossano Venturini
Venue: SIGIR
Year: 2007

Recently [Manning et al., 2007] resorted the Permuterm indexof Garfield (1976) as a time-efficient and elegant solution to the string dictionary problem in which pattern queries may possibly include one wild-card symbol (called, Tolerant Retrieval problem). Unfortunately the Permuterm index is space inefficient because its quadruples the dictionary size. In this paper we propose the Compressed Permuterm Index which solves the Tolerant Retrieval problem in optimal query time, i.e. time proportional to the length of the searched pattern, and space close to the k-th order empirical entropy of the indexed dictionary. Our index can be used to solve also more sophisticated queries which involve several wild-card symbols, or require to prefix-match multiple fields in a database of records.The result is based on an elegant variant of the Burrows-Wheeler Transform defined on a dictionary of strings of variable length, which allows to easily adapt known compressed indexes [Makinen-Navarro, 2007] to solve the Tolerant Retrieval problem. Experiments show that our index supports fast queries within a space occupancy that is close to the one achievable by compressing the string dictionary via gzip, bzip or ppmdi. This improves known approaches based on front-coding by more than 50% in absolute space occupancy, still guaranteeing comparable query time.
A summarisation logic for structured documents
Authors: Jan Frederik Forst
Venue: SIGIR
Year: 2007

The logical approach to Information Retrieval tries to model the relevance of a document given a query as the logical implication between documents and queries. In early work, van Rijsbergen states that the retrieval status value of a document given a query is proportional to the degree of implication between a document and a query. Based on this, several probabilistic logics for information retrieval have been conceived, which add an additional layer of abstraction to the information retrieval task: probabilistic models for the retrieval of documents are expressed in those logics, rather than implemented directly.
Story segmentation of broadcast news in Arabic, Chinese and English using multi-window features
Authors: Martin Franz, Jian-Ming Xu
Venue: SIGIR
Year: 2007

The paper describes a maximum entropy based story segmentation system for Arabic, Chinese and English. In experiments with broadcast news data from TDT-3, TDT-4, and corpora collected in the DARPA GALE project we obtain a substantial performance gain using multiple overlapping windows for text-based features.
Using collaborative queries to improve retrieval for difficult topics
Authors: Xin Fu, Diane Kelly, Chirag Shah
Venue: SIGIR
Year: 2007

We describe a preliminary analysis of queries created by 81 users for 4 topics from the TREC Robust Track. Our goal was to explore the potential benefits of using queries created by multiple users on retrieval performance for difficult topics. We first examine the overlap in users' queries and the overlap in results with respect to different queries for the same topic. We then explore the potential benefits of combining users' queries in various ways. Our results provide some evidence that having access to multiple users' queries can improve retrieval for individual searchers and for difficult topics.
Feature selection for ranking
Authors: Xiubo Geng, Tie-Yan Liu, Tao Qin, Hang Li
Venue: SIGIR
Year: 2007

Ranking is a very important topic in information retrieval. While algorithms for learning ranking models have been intensively studied, this is not the case for feature selection, despite of its importance. The reality is that many feature selection methods used in classification are directly applied to ranking. We argue that because of the striking differences between ranking and classification, it is better to develop different feature selection methods for ranking. To this end, we propose a new feature selection method in this paper. Specifically, for each feature we use its value to rank the training instances, and define the ranking accuracy in terms of a performance measure or a loss function as the importance of the feature. We also define the correlation between the ranking results of two features as the similarity between them. Based on the definitions, we formulate the feature selection issue as an optimization problem, for which it is to find the features with maximum total importance scores and minimum total similarity scores. We also demonstrate how to solve the optimization problem in an efficient way. We have tested the effectiveness of our feature selection method on two information retrieval datasets and with two ranking models. Experimental results show that our method can outperform traditional feature selection methods for the ranking task.
ISKODOR: unified user modeling for integrated searching
Authors: Melanie Gnasa, Armin B. Cremers, Douglas W. Oard
Venue: SIGIR
Year: 2007

ISKODOR integrates personal collections, peer search, and centralized search services. User modeling in ISKODOR fills three roles: discovery of sites with suitable information stores, context-based query interpretation, and automatic profile-based filtering of new information. Explanation and control are achieved through graphical depiction of sources, explicit feedback regard ingutility, and explicit control over peer association behavior and information sharing.
Confluence: enhancing contextual desktop search
Authors: Karl Gyllstrom, Craig A. N. Soules, Alistair C. Veitch
Venue: SIGIR
Year: 2007

We present Confluence, an enhancement to a desktop file search tool called Confluence which extracts conceptual relationships between files by their temporal access patterns in the file system. A limitation of a purely file-based approach is that as file operations are increasingly abstracted by applications, their correlation to a user's activity weakens and thereby reduces the applicability of their temporal patterns. To deal with this problem, we augment the file event stream with a stream of window focus events from the UI layer. We present 3 algorithms that analyze this new stream, extracting the user's task information which informs the existing Confluence algorithms. We present results and conclusions from a preliminary user study on Confluence.
Selection and ranking of text from highly imperfect transcripts for retrieval of video content
Authors: Alexander Haubold
Venue: SIGIR
Year: 2007

In the domain of video content retrieval, we present an approach for selecting words and phrases from highly imperfect automatically generated transcripts. Extracted terms are ranked according to their descriptiveness and presented to the user in a multimedia browser interface. We use sense querying from the WordNet lexical database for our method of text selection and ranking. Evaluation of 679 video summarization tasks from 442 users shows that the method of ranking and emphasizing terms according to descriptiveness results in higher accuracy responses in less time compared to the baseline of no ranking.
Laplacian optimal design for image retrieval
Authors: Xiaofei He, Wanli Min, Deng Cai, Kun Zhou
Venue: SIGIR
Year: 2007

Relevance feedback is a powerful technique to enhance Content-Based Image Retrieval (CBIR) performance. It solicits the user’s relevance judgments on the retrieved images returned by the CBIR systems. The user’s labeling is then used to learn a classifier to distinguish between relevant and irrelevant images. However, the top returned images may not be the most informative ones. The challenge is thus to determine which unlabeled images would be the most informative (i.e., improve the classifier the most) if they were labeled and used as training samples. In this paper, we propose a novel active learning algorithm, called Laplacian Optimal Design (LOD), for relevance feedback image retrieval. Our algorithm is based on a regression model which minimizes the least square error on the measured (or, labeled) images and simultaneously preserves the local geometrical structure of the image space. Specifically, we assume that if two images are sufficiently close to each other, then their measurements (or, labels) are close as well. By constructing a nearest neighbor graph, the geometrical structure of the image space can be described by the graph Laplacian. We discuss how results from the field of optimal experimental design may be used to guide our selection of a subset of images, which gives us the most amount of information. Experimental results on Corel database suggest that the proposed approach achieves higher precision in relevance feedback image retrieval.
Radio Oranje: searching the queen's speech(es)
Authors: Willemijn Heeren, Laurens van der Werff, Roeland Ordelman, Arjan van Hessen, Franciska de Jong
Venue: SIGIR
Year: 2007

The 'Radio Oranje' demonstrator shows an attractive multimedia user experience in the cultural heritage domain based on a collection of mono-media audio documents. It supports online search and browsing of the collection using indexing techniques, specialized content visualizations and a related photo database.
Automatic extension of non-english wordnets
Authors: Katja Hofmann, Erik F. Tjong Kim Sang
Venue: SIGIR
Year: 2007

Lexical taxonomies, such as WordNet, are important resources underlying natural language processing techniques such as machine translation and word-sense disambiguation. However, creating and maintaining such taxonomies manually is a tedious and time-consuming task. This has led to a great deal of interest in automatic methods for retrieving taxonomic relations. Efforts for both manual development of taxonomies and automatic acquisition methods have largely focused on English-language resources. Although WordNets exist for many languages, these are usually much smaller than Princeton WordNet (PWN) [1], the major semantic resource for English. For English, an excellent method has been developed for automatically extending lexical taxonomies. Snow et al. [4] show that it is possible to predict new and precise hypernymhyponym relations from a parsed corpus. Unfortunately, the method cannot be easily applied to other languages, as it relies on existing lexical resources. First, the authors use the large amount of data already contained in the PWN to extract a pattern lexicon and train their hypernym classifier. Second, the authors apply word-sense disambiguation based on an existing sense-tagged corpus. Problems in transferring the method to another language include the small size of non-English WordNets and the lack of sense-tagged corpora in other languages. In this paper we apply the method of [4] to a non-English language (Dutch) for which only a basic WordNet and a parser are available. We find that, without an additional sense-tagged corpus, this approach is highly susceptible to noise due to word sense ambiguity. We propose and evaluate two methods to address this problem.
Locality discriminating indexing for document classification
Authors: Jiani Hu, Weihong Deng, Jun Guo, Weiran Xu
Venue: SIGIR
Year: 2007

This paper introduces a locality discriminating indexing (LDI) algorithm for document classification. Based on the hypothesis that samples from different classes reside in class-specific manifold structures, LDI seeks for a projection which best preserves the within-class local structures while suppresses the between-class overlap. Comparative experiments show that the proposed method isable to derives compact discriminating document representations for classification.
Dimensionality reduction for dimension-specific search
Authors: Zi Huang, Heng Tao Shen, Xiaofang Zhou, Dawei Song, Stefan M. Rüger
Venue: SIGIR
Year: 2007

Dimensionality reduction plays an important role in efficient similarity search, which is often based on k-nearest neighbor (k-NN) queries over a high-dimensional feature space. In this paper, we introduce a novel type of k-NN query, namely conditional k-NN (ck-NN), which considers dimension-specific constraint in addition to the inter-point distances. However, existing dimensionality reduction methods are not applicable to this new type of queries. We propose a novel Mean-Std (standard deviation) guided Dimensionality Reduction (MSDR) to support a pruning based efficient ck-NN query processing strategy. Our preliminary experimental results on 3D protein structure data demonstrate that the MSDR method is promising.
Multiple-signal duplicate detection for search evaluation
Authors: Scott B. Huffman, April Rasala Lehman, Alexei P. Stolboushkin, Howard Wong-Toi, Fan Yang, Hein Röhrig
Venue: SIGIR
Year: 2007

We consider the problem of duplicate document detection for search evaluation. Given a query and a small number of web results for that query, we show how to detect duplicate web documents with precision ~0.91 and recall ~77. In contrast, Charikar's algorithm, designed for duplicate detection in an indexing pipeline, achieves precision ~0.91 but with a recall of ~0.58. Our improvement in recall while maintaining high precision comes from combining three ideas. First, because we are only concerned with duplicate detection among results for the same query, the number of pairwise comparisons is small. Therefore we can afford to compute multiple pairwise signals for each pair of documents. A model learned with standard machine-learning techniques improves recall to ~0.68 with precision ~0.90. Second, most duplicate detection has focused on text analysis of the HTML contents of a document. In some web pages the HTML is not a good indicator of the final contents of the page. We use extended fetching techniques to fill in frames and execute Java script. Including signals based on our richer fetches further improves the recall to ~0.75 and the precision to ~0.91. Finally, we also explore using signals based on the query. Comparing contextual snippets based on the richer fetches improves the recall to ~0.77. We show that the overall accuracy of this final model approaches that of human judges.
Information-behaviour modeling with external cues
Authors: Michael Huggett
Venue: SIGIR
Year: 2007

Much of human activity defines an information context. We awaken, start work, and hold meetings at roughly the same time every day, and retrieve the same information items (day planners, itineraries, schedules, agendas, reports, menus, web pages, etc.) for many of these activities. Information retrieval systems in general lack sensitivity to such recurrent context, requiring users to remember and re-enter search cues for objects regardless of how regularly or consistently the objects are used, and to develop ad-hoc storage strategies.
Strategy follows technology
Authors: Edwin van Huis
Venue: SIGIR
Year: 2007

In strategic management there has been a debate over many years. Already in 1962 Alfred Chandler had stated: Structure follows Strategy. In the nineteen eighties, Michael Porter modified Chandler's dictum about structure following strategy by introducing a second level of structure: organizational structure follows strategy, which in turn follows structure. So the question became: what is leading what?.
Investigating the relevance of sponsored results for web ecommerce queries
Authors: Bernard J. Jansen
Venue: SIGIR
Year: 2007

Are sponsored links, the primary business model for Web search engines, providing Web consumers with relevant results? This research addresses this issue by investigating the relevance of sponsored and non-sponsored links for ecommerce queries from the major search engines. The results show that average relevance ratings for sponsored and non-sponsored links are virtually the same, although the relevance ratings for sponsored links are statistically higher. We used 108 ecommerce queries and 8,256 retrieved links for these queries from three major Web search engines, Google, MSN, and Yahoo!. We present the implications for Web search engines and sponsored search as a long-term business model as well as a mechanism for finding relevant information for searchers.
Viewing online searching within a learning paradigm
Authors: Bernard J. Jansen, Brian Keith Smith, Danielle L. Booth
Venue: SIGIR
Year: 2007

In this research, we investigate whether one can model online searching as a learning paradigm. We examined the searching characteristics of 41 participants engaged in 246 searching tasks. We classified the searching tasks according to Anderson and Krathwohl's Taxonomy, an updated version of Bloom's taxonomy. Anderson and Krathwohl is a six level categorization of cognitive learning. Research results show that Applying takes the most searching effort as measured by queries per session and specific topics searched per sessions. The categories of Remembering and Understanding, which are lower-order learning levels, exhibit searching characteristics similar to the higher order categories of Evaluating and Creating. It seems that searchers rely primarily on their internal knowledge and use searching primarily as fact checking and verification when engaged in Evaluating and Creating. Implications are that the commonly held notions of Web searchers having simple information goals may not be correct. We discuss the implications for Web searching, including designing interfaces to support exploration and alternate views.
Protecting source privacy in federated search
Authors: Wei Jiang, Luo Si, Jing Li
Venue: SIGIR
Year: 2007

Many information sources contain information that can only be accessed through search-specific search engines. Federated search provides search solutions of this type of hidden information that cannot be searched by conventional search engines. In many scenarios of federated search, such as the search among health care providers or among intelligence agencies, an individual information source does not want to disclose the source of the search results to users or other sources. Therefore, this paper proposes a two-step federated search protocol that protects the privacy of information sources. As far as we know, this is the first attempt to address the research problem of protecting source privacy in federated text search.
Natural language and the information layer
Authors: Karen Spärck Jones
Venue: SIGIR
Year: 2007

Article Share on Natural language and the information layer Author: Karen Spärck Jones University of Cambridge, Cambridge, UK University of Cambridge, Cambridge, UKView Profile Authors Info & Claims SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrievalJuly 2007 Pages 3–6https://doi.org/10.1145/1277741.1277744Online:23 July 2007Publication History 0citation689DownloadsMetricsTotal Citations0Total Downloads689Last 12 Months6Last 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 SiteGet Access
Where to start reading a textual XML document?
Authors: Jaap Kamps, Marijn Koolen, Mounia Lalmas
Venue: SIGIR
Year: 2007

In structured information retrieval, the aim is to exploit document structure to retrieve relevant components, allowing the user to go straight to the relevant material. This paper looks at the so-called best entry points (BEPs), which are intended to give the user the best starting point to access the relevant information in the document. We examine the relationship between BEPs and relevant components in the INEX 2006 ad hoc assessments. Our main findings are the following: First, although documents are short, assessors often choose the best entry point some distance from the start of the document. Second, many of the best entry points coincide with the first relevant character in relevant documents, showing a strong relation between the BEP and relevant text. Third, we find browsing BEPs in articles with a single relevant passages, and container BEPs or context BEPs in articles with more relevant passages.