OpenRaiser/Intern-Atlas huggingface dataset:
Discovering and representing the contextual and narrative structure of e-books to support reading and comprehension (abstract only)
Authors: Yixing Sun
Venue: SIGIR
Year: 2004

A person reading a book needs to build an understanding based on the available textual materials. As a result of a survey of users' reading behaviours and of existing e-Book user interfaces, we found that most of these interfaces provide poor support for the actual processes of reading and comprehension. In particular, there is generally minimal support for understanding the overall structure (or contextual structure) and the narrative structure of a book. We propose adapting topic tracking and detection techniques to discover the narrative threads within a book, and hence its narrative structure. The contextual and narrative structures will be presented to the user through purpose-designed visualisations, which will be integrated and linked within a newly developed e-Book browser. We have chosen to use the Bible as our test corpus, as it has a rich narrative structure, and relatively complex contextual structure. Evaluation of the interface, and its components, will be done through field studies involving actual readers of the Bible, to assess the effectiveness of the user interface in enhancing a user's experience.
Design of an e-book user interface and visualizations to support reading for comprehension
Authors: Yixing Sun, David J. Harper, Stuart N. K. Watt
Venue: SIGIR
Year: 2004

Current e-Book browsers provide minimal support for comprehending the organization, narrative structure, and themes, of large complex books. In order to build an understanding of such books, readers should be provided with user interfaces that present, and relate, the organizational, narrative and thematic structures. We propose adapting information retrieval techniques for the purpose of discovering these structures, and sketch three distinctive visualizations for presenting these structures to the e-Book reader. These visualizations are presented within an initial design for an e-Book browser.
On scaling latent semantic indexing for large peer-to-peer systems
Authors: Chunqiang Tang, Sandhya Dwarkadas, Zhichen Xu
Venue: SIGIR
Year: 2004

The exponential growth of data demands scalable infrastructures capable of indexing and searching rich content such as text, music, and images. A promising direction is to combine information retrieval with peer-to-peer technology for scalability, fault-tolerance, and low administration cost. One pioneering work along this direction is pSearch [32, 33]. pSearch places documents onto a peerto-peer overlay network according to semantic vectors produced using Latent Semantic Indexing (LSI). The search cost for a query is reduced since documents related to the query are likely to be co-located on a small number of nodes. Unfortunately, because of its reliance on LSI, pSearch also inherits the limitations of LSI. (1) When the corpus is large and heterogeneous, LSI’s retrieval quality is inferior to methods such as Okapi. (2) The Singular Value Decomposition (SVD) used in LSI is unscalable in terms of both
A two-stage mixture model for pseudo feedback
Authors: Tao Tao, ChengXiang Zhai
Venue: SIGIR
Year: 2004

Pseudo feedback is a commonly used technique to improve information retrieval performance. It assumes a few top-ranked documents to be relevant, and learns from them to improve the retrieval accuracy. A serious problem is that the performance is often very sensitive to the number of pseudo feedback documents. In this poster, we address this problem in a language modeling framework. We propose a novel twostage mixture model, which is less sensitive to the number of pseudo feedback documents than an effective existing feedback model. The new model can tolerate a more flexible setting of the number of pseudo feedback documents without the danger of losing much retrieval accuracy.
An artificial intelligence approach to information retrieval (abstract only)
Authors: Andrew Trotman
Venue: SIGIR
Year: 2004

Current approaches to information retrieval rely on the creativity of individuals to develop new algorithms. In this investigation the use of genetic algorithms (GA) and genetic programming (GP) to learn IR algorithms is examined.Document structure weighting is a technique whereby different parts of a document (title, abstract, etc.) contribute unevenly to the overall document weight during ranking. Near optimal weights can be learned with a GA. Doing so shows a statistically significant 5% relative improvement in MAP for vector space inner product and Croft's probabilistic ranking, but no improvement for BM25. Two applications of this approach are suggested: offline learning, and relevance feedback.In a second set of experiments, a new ranking function was learned using GP. This new function yields a statistically significant 11% relative improvement on unseen queries tested on the training documents. Portability tests to different collections (not used in training) demonstrate the performance of the new function exceeds vector space and probability, and slightly exceeds BM25. Learning weights for this new function is proposed.The application of genetic learning to stemming and thesaurus construction is discussed. Stemming rules such as those of the Porter algorithm are candidates for GP learning whereas synonym sets are candidates for GA learning.
Filtering algorithms for information retrieval models with named attributes and proximity operators
Authors: Christos Tryfonopoulos, Manolis Koubarakis, Yannis Drougas
Venue: SIGIR
Year: 2004

In the selective dissemination of information (or publish/subscribe) paradigm, clients subscribe to a server with continuous queries (or profiles) that express their information needs. Clients can also publish documents to servers. Whenever a document is published, the continuous queries satisfying this document are found and notifications are sent to appropriate clients. This paper deals with the filtering problem that needs to be solved effciently by each server: Given a database of continuous queries db and a document d, find all queries q ∈ db that match d. We present data structures and indexing algorithms that enable us to solve the filtering problem efficiently for large databases of queries expressed in the model AWP which is based on named attributes with values of type text, and word proximity operators.
Verifying a Chinese collection for text categorization
Authors: Yuen-Hsien Tseng, William John Teahan
Venue: SIGIR
Year: 2004

This article describes the development of a free test collection for Chinese text categorization. A novel retrieval-based approach was developed to detect duplicates and label inconsistency in this corpus and in Reuters-21578 for comparison. The method was able to detect certain types of similar and/or duplicated documents that were overlooked by an alternative repetition-based method [1]. Experiments showed that effectiveness was not affected by the confusing documents.
Measuring ineffectiveness
Authors: Ellen M. Voorhees
Venue: SIGIR
Year: 2004

An evaluation methodology that targets ineffective topics is needed to support research on obtaining more consistent retrieval across topics. Using average values of traditional evaluation measures is not an appropriate methodology be-cause it emphasizes effective topics: poorly performing top-ics ’ scores are by definition small, and they are therefore difficult to distinguish from the noise inherent in retrieval evaluation. We examine two new measures that empha-size a system’s worst topics. While these measures focus on different aspects of retrieval behavior than traditional mea-sures, the measures are less stable than traditional measures and the margin of error associated with the new measures is large relative to the observed differences in scores.
A review of relevance feedback experiments at the 2003 reliable information access (RIA) workshop
Authors: Robert H. Warren, Ting Liu
Venue: SIGIR
Year: 2004

We review here the results of one of the experiments performed at the 2003 Reliable Information Access (RIA) Workshop, hosted by Mitre Corporation and the Northeast Regional Research Center (NRRC). The experiment concentrates on query expansion using relevance feedback and explores the behaviour of several information retrieval systems using variable numbers of relevant documents.
Probabilistic model for contextual retrieval
Authors: Ji-Rong Wen, Ni Lao, Wei-Ying Ma
Venue: SIGIR
Year: 2004

Contextual retrieval is a critical technique for facilitating many important applications such as mobile search, personalized search, PC troubleshooting, etc. Despite of its importance, there is no comprehensive retrieval model to describe the contextual retrieval process. We observed that incompatible context, noisy context and incomplete query are several important issues commonly existing in contextual retrieval applications. However, these issues have not been previously explored and discussed. In this paper, we propose probabilistic models to address these problems. Our study clearly shows that query log is the key to build effective contextual retrieval models. We also conduct a case study in the PC troubleshooting domain to testify the performance of the proposed models and experimental results show that the models can achieve very good retrieval precision.
A study of topic similarity measures
Authors: Ryen W. White, Joemon M. Jose
Venue: SIGIR
Year: 2004

In this poster we describe an investigation of topic similarity measures. We elicit assessments on the similarity of 10 pairs of topic from 76 subjects and use these as a benchmark to assess how well each measure performs. The measures have the potential to form the basis of a predictive technique, for adaptive search systems. The results of our evaluation show that measures based on the level of correlation between topics concords most with general subject perceptions of search topic similarity.
An implicit system for predicting interests
Authors: Ryen W. White, Joemon M. Jose
Venue: SIGIR
Year: 2004

We demonstrate an adaptive search system that works proactively to help searchers find relevant information. The system observes searcher interaction, uses what it sees to model information needs and chooses additional query terms. The system watches for changes in the topic of the search and selects retrieval strategies that reflect the extent to which the topic is seen to change.
Collaborative filing in a document repository
Authors: Harris Wu, Michael D. Gordon
Venue: SIGIR
Year: 2004

We introduce an emergent, collaborative filing system. In such a system, an individual is allowed to organize a subset of documents in a repository into a personal hierarchy and share the hierarchy with others. The system generates a "consensus" hierarchy from all users' personal hierarchies, which provides a full, common, and emergent view of all documents. We believe that collaborative filing helps translate personal, tacit knowledge into sharable structures, which help the user as well a community of which he or she is a part. Our filing system is suitable for any documents from text to multimedia files. Initial results on an experimental website show promise. For a knowledge task involving extensive document retrieval, hierarchies are not only used frequently but are also effective in identifying high quality documents. One surprising finding is how often subjects use others' personal hierarchies, and upon close examination, social networks play a key role as well.
Human versus machine in the topic distillation task
Authors: Mingfang Wu, Gheorghe Muresan, Alistair McLean, Muh-Chyun (Morris) Tang, Ross Wilkinson, Yuelin Li, Hyuk-Jin Lee, Nicholas J. Belkin
Venue: SIGIR
Year: 2004

This paper reports on and discusses a set of user experiments using the TREC 2003 Web interactive track protocol. The focus is on comparing humans and machine algorithms in terms of performance in a topic distillation task. We also investigated the effect of the search results layout in supporting the users' effort.We have demonstrated that machines can perform nearly as well as people on the topic distillation task. Given a system tailored to the task there is significant performance improvement and finally, given a presentation that supports the task, there is strong user satisfaction.
Document clustering by concept factorization
Authors: Wei Xu, Yihong Gong
Venue: SIGIR
Year: 2004

In this paper, we propose a new data clustering method called concept factorization that models each concept as a linear combination of the data points, and each data point as a linear combination of the concepts. With this model, the data clustering task is accomplished by computing the two sets of linear coefficients, and this linear coefficients computation is carried out by finding the non-negative solution that minimizes the reconstruction error of the data points. The cluster label of each data point can be easily derived from the obtained linear coefficients. This method differs from the method of clustering based on non-negative matrix factorization (NMF) \citeXu03 in that it can be applied to data containing negative values and the method can be implemented in the kernel space. Our experimental results show that the proposed data clustering method and its variations performs best among 11 algorithms and their variations that we have evaluated on both TDT2 and Reuters-21578 corpus. In addition to its good performance, the new method also has the merit in its easy and reliable derivation of the clustering results.
A unified model of literal mining and link analysis for ranking web resources
Authors: Yinghui Xu, Kyoji Umemura
Venue: SIGIR
Year: 2004

Web link analysis has been proved to provide significant enhancement to the precision of Web search in practice. The PageRank algorithm, which is used in Google Search Engine, plays an important role on improving the quality of its resuls by employing the explicit hyperlink structure among the Web pages. The prestige of Web pages defined by PageRank is purely derived from surfer random walk on the Web graph without textual content content consideration. However, in the practical sense, user surfing behavior is far from random jumping. In this paper, we present a unified model for a more accurate page rank. User's surfing is guided by a probabilistic model that is based on literal matching between connected pages. The result shows that our proposed ranking algorithms do perform better than the original PageRank.
Effectiveness of web page classification on finding list answers
Authors: Hui Yang, Tat-Seng Chua
Venue: SIGIR
Year: 2004

List question answering (QA) offers a unique challenge in effectively and efficiently locating a complete set of distinct answers from huge corpora or the Web. In TREC-12, the median average F1 performance of list QA systems was only 6.9%. This paper exploits the wealth of freely available text and link structures on the Web to seek complete answers to list questions. We employ natural language parsing, web page classification and clustering to find reliable list answers. We also study the effectiveness of web page classification on both the recall and uniqueness of answers for web-based list QA.
Aggregated feature retrieval for MPEG-7 via clustering
Authors: Jiamin Ye, Alan F. Smeaton
Venue: SIGIR
Year: 2004

In this paper, we describe an approach to combining text and visual features from MPEG-7 descriptions of video. A video retrieval process is aligned to a text retrieval process based on the TF*IDF vector space model via clustering of low-level visual features. Our assumption is that shots within the same cluster are not only similar visually but also semantically, to a certain extent. Our experiments on the TRECVID2002 and TRECVID2003 collections show that adding extra meaning to a shot based on the shots from the same cluster is useful when each video in a collection contains a high proportion of similar shots, for example in documentaries.
Supporting multiple information-seeking strategies in a single system framework (abstract only)
Authors: Xiaojun Yuan
Venue: SIGIR
Year: 2004

This research explores the relationship between information-seeking strategies (ISSs) and information retrieval (IR) system design. When people seek information they engage in a variety of ISSs in order to search for specific items, learn about the contents of the database, evaluate retrieved information, and so on.The theoretical foundations of the work are based on the information-seeking episode model developed by Belkin (1996), and the multi-facet classification scheme of information behaviors proposed by Cool & Belkin (2002).The goal of this research is to construct and evaluate an interactive retrieval system which uses different combinations of IR techniques to support different ISSs. Example IR techniques include comparison using exact and probabilistic matching algorithms; summarization of information objects using titles, snippets or abstracts; visualization techniques such as lists or classified results; and navigation techniques such as scrolling or following links. By designing a retrieval system with diverse strategies in mind, we can adaptively support multiple ISSs, permitting a user to move seamlessly from one strategy to another, choosing instantiations of each support technique tailored to the specific ISS.The research will be conducted in a series of four steps. (1) Develop an object-oriented framework for representing basic IR techniques. (2) Design, implement and evaluate systems which support individual ISSs such as browsing and searching. (3) Specify an interaction structure for guiding and controlling sequences of different supporting techniques.(4) Design, implement, and evaluate a dynamically adaptive system supporting multiple ISSs in comparison to a non-adaptive baseline system.
Automatic sense disambiguation for acronyms
Authors: Manuel Zahariev
Venue: SIGIR
Year: 2004

A machine learning methodology for the disambiguation of acronym senses is presented, which starts from an acronym sense dictionary. Training data is automatically extracted from downloaded documents identified from the results of search engine queries. Leave-one-out cross-validation on 9,963 documents with 47 acronym forms achieves accuracy 92.58% and Fß=1=91.52%.
Using bayesian priors to combine classifiers for adaptive filtering
Authors: Yi Zhang
Venue: SIGIR
Year: 2004

An adaptive information filtering system monitors a document stream to identify the documents that match information needs specified by user profiles. As the system filters, it also refines its knowledge about the user's information needs based on long-term observations of the document stream and periodic feedback(training data) from the user. Low variance profile learning algorithms, such as Rocchio, work well at the early stage of filtering when the system has very few training data. Low bias profile learning algorithms, such as Logistic Regression, work well at the later stage of filtering when the system has accumulated enough training data.However, an empirical system needs to works well consistently at all stages of filtering process. This paper addresses this problem by proposing a new technique to combine different text classification algorithms via a constrained maximum likelihood Bayesian prior. This technique provides a trade off between bias and variance, and the combined classifier may achieve a consistent good performance at different stages of filtering. We implemented the proposed technique to combine two complementary classification algorithms: Rocchio and logistic regression. The new algorithm is shown to compare favorably with Rocchio, Logistic Regression, and the best methods in the TREC-9 and TREC-11 adaptive filtering tracks.
Web taxonomy integration through co-bootstrapping
Authors: Dell Zhang, Wee Sun Lee
Venue: SIGIR
Year: 2004

We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only currently pervasive on the web, but also important to the emerging semantic web. A straightforward approach to automating this process would be to learn a classifier that can classify objects from the source taxonomy into categories of the master taxonomy. The key insight is that the availability of the source taxonomy data could be helpful to build better classifiers for the master taxonomy if their categorizations have some semantic overlap. In this paper, we propose a new approach, co-bootstrapping, to enhance the classification by exploiting such implicit knowledge. Our experiments with real-world web data show substantial improvements in the performance of taxonomy integration.
Focused named entity recognition using machine learning
Authors: Li Zhang, Yue Pan, Tong Zhang
Venue: SIGIR
Year: 2004

In this paper we study the problem of finding most topical named entities among all entities in a document, which we refer to as focused named entity recognition. We show that these focused named entities are useful for many natural language processing applications, such as document summarization, search result ranking, and entity detection and tracking. We propose a statistical model for focused named entity recognition by converting it into a classification problem. We then study the impact of various linguistic features and compare a number of classification algorithms. From experiments on an annotated Chinese news corpus, we demonstrate that the proposed method can achieve near human-level accuracy.
Using the web for automated translation extraction in cross-language information retrieval
Authors: Ying Zhang, Phil Vines
Venue: SIGIR
Year: 2004

There have been significant advances in Cross-Language Information Retrieval (CLIR) in recent years. One of the major remaining reasons that CLIR does not perform as well as monolingual retrieval is the presence of out of vocabulary (OOV) terms. Previous work has either relied on manual intervention or has only been partially successful in solving this problem. We use a method that extends earlier work in this area by augmenting this with statistical analysis, and corpus-based translation disambiguation to dynamically discover translations of OOV terms. The method can be applied to both Chinese-English and English-Chinese CLIR, correctly extracting translations of OOV terms from the Web automatically, and thus is a significant improvement on earlier work.
Analysis of factoid questions for effective relation extraction
Authors: Eugene Agichtein, Silviu Cucerzan, Eric Brill
Venue: SIGIR
Year: 2005

We present an analysis of the structured relationships observed in a randomly sampled set of question-like queries submitted to a search engine for a popular online encyclopedic document collection. Our study shows that a relatively small number of binary relationships account for most of the queries in the sample. This empirically validates an approach of analyzing query logs to identify the relationships most relevant to user needs and populating corresponding fact tables from the collection for factoid question answering. Our analysis shows that such an approach can lead to substantial coverage of user questions.