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Mucins are important sources of carbohydrate for bacteria growing in the human large intestine. As well as being produced by goblet cells in the colonic mucosa, salivary, gastric, biliary, bronchial, and small intestinal mucins also enter the colon in effluent from the small bowel. Particulate matter, such as partly digested plant cell materials, are entrapped in this viscoelastic gel, which must be broken down to facilitate access of intestinal microorganisms to the food residues. It is estimated that between 2 to 3 g of mucin enter the large bowel each day from the upper digestive tract (1), however, the rate of colonic mucus formation is unknown. Complex polymers, such as mucin must be degraded by a wide range of hydrolytic enzymes to smaller oligomers and their component sugars and amino acids before they can be assimilated by intestinal bacteria. [ABSTRACT FROM AUTHOR]

In 2000, the European Society of Cardiology and the American College of Cardiology jointly published [1] guidelines defining myocardial infarction. The definition was based partly on the availability of new biomarkers to assist in detecting myocardial damage. As part of their report, the criteria for acute ST elevation myocardial infarction were established. These provided thresholds for abnormal ST elevation in the 12-lead ECG. Table 1 summarises these criteria. [ABSTRACT FROM AUTHOR]

In recent years, creating alternative computer environments, especially Tangible User Interfaces (TUIs) for children, has become increasingly popular. However, up till now, the evaluation of tangible technologies has been rather scarce. This paper focuses on evaluating children's technologies that go beyond the desktop computer. A qualitative case study on our TUI prototype will be presented by using selected methods: Think Aloud (TA), Peer Tutoring (PT) and Drawing Intervention (DI). We found some limitations to the methods and there were lessons that were learnt when the evaluation studies were carried out with children. The research will contribute to the paradigms, such as the design and evaluation of ‘the disappearing computer‘ and ‘tangible computing‘. [ABSTRACT FROM AUTHOR]

This is the first year for the Centre for Interactive Systems Research participation of INEX. Based on a newly developed XML indexing and retrieval system on Okapi, we extend Robertson's field-weighted BM25F for document retrieval to element level retrieval function BM25E. In this paper, we introduce this new function and our experimental method in detail, and then show how we tuned weights for our selected fields by using INEX 2004 topics and assessments. Based on the tuned models we submitted our runs for CO.Thorough, CO.FetchBrowse, the methods we propose show real promise. Existing problems and future work are also discussed. [ABSTRACT FROM AUTHOR]

Structured document retrieval requires the ranking of document elements. Previous approaches either aggregate term weights or retrieval status values, or propose alternatives to , for example, (inverse element frequency). We propose and investigate in this paper a new approach: Context-specific , which is, in contrast to aggregation-based ranking functions, parameter-free. [ABSTRACT FROM AUTHOR]

A new Relevance Feedback (RF) technique is developed to improve upon the efficiency and performance of existing techniques. This is based on propagating positive and negative weights from documents judged relevant and not relevant respectively, to other documents, which are deemed similar according to one of a number of criteria. The performance and efficiency improve since the documents are treated as independent vectors rather than being merged into a single vector as is the case with traditional approaches, and only the documents considered in a given neighbourhood are inspected. This is especially important when using large test collections. [ABSTRACT FROM AUTHOR]

Current information retrieval systems make no measurement of the user's response to the searching process or the information itself. Existing psychological studies show that subjects exhibit measurable physiological responses when carrying out certain tasks, e.g. when viewing images, which generally result in heightened emotional states. We find that users exhibit measurable biometric behaviour in the form of galvanic skin response when watching movies, and engaging in interactive tasks. We examine how this data might be exploited in the indexing of data for search and within the search process itself. [ABSTRACT FROM AUTHOR]

Current research in the field of automatic plagiarism detection for text documents focuses on algorithms that compare plagiarized documents against potential original documents. Though these approaches perform well in identifying copied or even modified passages, they assume a closed world: a reference collection must be given against which a plagiarized document can be compared. This raises the question whether plagiarized passages within a document can be detected automatically if no reference is given, e. g. if the plagiarized passages stem from a book that is not available in digital form. We call this problem class intrinsic plagiarism detection. The paper is devoted to this problem class; it shows that it is possible to identify potentially plagiarized passages by analyzing a single document with respect to variations in writing style. Our contributions are fourfold: i a taxonomy of plagiarism delicts along with detection methods, ii new features for the quantification of style aspects, iii a publicly available plagiarism corpus for benchmark comparisons, and iv promising results in non-trivial plagiarism detection settings: in our experiments we achieved recall values of 85% with a precision of 75% and better. Keywords: plagiarism detection, style analysis, classifier, plagiarism corpus. [ABSTRACT FROM AUTHOR]

WWW Video Search Engines have become increasingly commonplace within the last few years and at the same time video retrieval research has been receiving more attention with the annual TRECVid series of workshops. In this paper we evaluate methods of relevance feedback for video search engines operating over TV news data. We show for both video shots and TV news stories, that an optimal number of terms can be identified to compose a new query for feedback and that in most cases; the number of documents employed for feedback does not have a great effect on these optimal numbers of terms. [ABSTRACT FROM AUTHOR]

Personal information needs depend on long-term interests and on current and future situations (contexts): people are mainly interested in weather forecasts for future destinations, and in toy advertisements when a child's birthday approaches. As computer capabilities for being aware of users' contexts grow, the users' willingness to set manually rules for context-based information retrieval will decrease. Thus computers must learn to associate user contexts with information needs in order to collect and present information proactively. This work presents experiments with training a SVM (Support Vector Machines) classifier to learn user information needs from calendar information. [ABSTRACT FROM AUTHOR]

Rank aggregation is a pervading operation in IR technology. We hypothesize that the performance of score-based aggregation may be affected by artificial, usually meaningless deviations consistently occurring in the input score distributions, which distort the combined result when the individual biases differ from each other. We propose a score-based rank aggregation model where the source scores are normalized to a common distribution before being combined. Early experiments on available data from several TREC collections are shown to support our proposal. [ABSTRACT FROM AUTHOR]

Geographic Information Retrieval (GIR) is concerned with the retrieval of documents based on both thematic and geographic content. An important issue in GIR, as for all IR, is relevance. In this paper we argue that spatial relevance should be considered independently from thematic relevance, and propose an initial scheme. A pilot study to assess this relevance scheme is presented, with initial results suggesting that users can distinguish between these two relevance dimensions, and that furthermore they have different properties. We suggest that spatial relevance requires greater assessor effort and more localised geographic knowledge than judging thematic relevance. [ABSTRACT FROM AUTHOR]

We present a prototype system using array comprehensions to bridge the gap between databases and information retrieval. It allows researchers to express their retrieval models in the General Matrix Framework for Information Retrieval [1], and have these executed on relational database systems with negligible effort. [ABSTRACT FROM AUTHOR]

We examine the impact on classification effectiveness of semantic differences in categories. Specifically, we measure broadness and narrowness of categories in terms of their distance to the root of a hierarchically organized thesaurus. Using categories of four different levels degrees of broadness, we show that classifying documents into narrow categories gives better scores than classifying them into broad terms, which we attribute to the fact that more specific categories are associated with terms with a higher discriminatory power. [ABSTRACT FROM AUTHOR]

We investigate the use of clustering methods for the task of grouping the text spans in a news article that refer to the same event. We provide evidence that the order in which events are described is structured in a way that can be exploited during clustering. We evaluate our approach on a corpus of news articles describing events that have occurred in the Iraqi War. [ABSTRACT FROM AUTHOR]

We present first steps towards intelligent retrieval of music album covers from the web. The continuous growth of electronic music distribution constantly increases the interest in methods to automatically provide added value like lyrics or album covers. While existing approaches rely on large proprietary databases, we focus on methods that make use of the whole web by using Google's or A9.com's image search. We evaluate the current state of the approach and point out directions for further improvements. [ABSTRACT FROM AUTHOR]

Image and video retrieval are both currently dominated by approaches which combine the outputs of several different representations or features. The ways in which the combination can be done is an established research problem in content-based image retrieval (CBIR). These approaches vary from image clustering through to semantic frameworks and mid-level visual features to ultimately determine sets of relative weights for the non-linear combination of features. Simple approaches to determining these weights revolve around executing a standard set of queries with known relevance judgements on some form of training data and is iterative in nature. Whilst successful, this requires both training data and human intervention to derive the optimal weights. [ABSTRACT FROM AUTHOR]

Readability refers to all characteristics of a document that contribute to its ‘ease of understanding or comprehension due to the style of writing' [1]. The readability of a text is dependent on a number of factors, including but not constrained to; its legibility, syntactic difficulty, semantic difficulty and the organization of the text [2]. As many as 228 variables were found to influence the readability of a text in Gray and Leary's seminal study [2]. These variables were classified as relating to document content, style, format or, features of organization. [ABSTRACT FROM AUTHOR]

We examine the use of authorship information in information retrieval for closed communities by extracting expert rankings for queries. We demonstrate that these rankings can be used to re-rank baseline search results and improve performance significantly. We also perform experiments in which we base expertise ratings only on first authors or on all except the final authors, and find that these limitations do not further improve our re-ranking method. [ABSTRACT FROM AUTHOR]

Answering multiple-choice questions, where a set of possible answers is provided together with the question, constitutes a simplified but nevertheless challenging area in question answering research. This paper introduces and evaluates two novel techniques for answer selection. It furthermore analyses in how far performance figures obtained using the English language Web as data source can be transferred to less dominant languages on the Web, such as Arabic. Result evaluation is based on questions from both the English and the Arabic versions of the TV show "Who wants to be a Millionaire?" as well as on the TREC-2002 QA data. [ABSTRACT FROM AUTHOR]

Latent Semantic Indexing (LSI) is an established dimensionality reduction technique for Information Retrieval applications. However, LSI generated dimensions are not optimal in a classification setting, since LSI fails to exploit class labels of training documents. We propose an approach that uses class information to influence LSI dimensions whereby class labels of training documents are endoded as new terms, which are appended to the documents. When LSI is carried out on the augmented term-document matrix, terms pertaining to the same class are pulled closer to each other. Evaluation over experimental data reveals significant improvement in classification accuracy over LSI. The results also compare favourably with naive Support Vector Machines. [ABSTRACT FROM AUTHOR]

In this paper we describe the application of our statistical pattern classification approach to question answering (QA) to the rapid development of monolingual QA systems. We show how the approach has been applied successfully to QA in English, Japanese, Chinese, Russian and Swedish to form the basis of our publicly accessible web-based multilingual QA system at http://asked.jp. [ABSTRACT FROM AUTHOR]

Rule-based email filters mainly rely on the occurrence of critical words to classify spam messages. However, perceptive obfuscation techniques can be used to elude exact pattern matching. In this paper we propose a new technique for filtering obfuscated email spam that performs approximate pattern matching both on the original message and on its phonetic transcription. [ABSTRACT FROM AUTHOR]

We applied different clustering algorithms to the task of clustering multi-word terms in order to reflect a humanly built ontology. Clustering was done without the usual document co-occurrence information. Our clustering algorithm, CPCL (Classification by Preferential Clustered Link) is based on general lexico-syntactic relations which do not require prior domain knowledge or the existence of a training set. Results show that CPCL performs well in terms of cluster homogeneity and shows good adaptability for handling large and sparse matrices. [ABSTRACT FROM AUTHOR]

ADM (Average Distance Measure) is an IR effectiveness metric based on the assumptions of continuous relevance and retrieval. This paper presents some novel experimental results on two different test collections: TREC 8, re-assessed on 4-levels relevance judgments, and TREC 13 TeraByte collection. The results confirm that ADM correlation with standard measures is high, even when using less data, i.e., few documents. [ABSTRACT FROM AUTHOR]

Effectiveness is a primary concern in the information retrieval (IR) field. Various metrics for IR effectiveness have been proposed in the past; we take into account all the 44 metrics we are aware of, classifying them into a two-dimensional grid. The classification is based on the notions of relevance, i.e., if (or how much) a document is relevant, and retrieval, i.e., if (how much) a document is retrieved. To our knowledge, no similar classification has been proposed so far. [ABSTRACT FROM AUTHOR]

Collaborative Web search is a form of meta-search that manipulates the results of underlying Web search engines in response to the learned preferences of a given community of users. Results that have previously been selected in response to similar queries by community members are promoted in the returned results. However, promotion is limited to these previously-selected results and in this paper we describe and evaluate how relevant results without a selection history can also be promoted by exploiting snippet-text and title similarities. [ABSTRACT FROM AUTHOR]

The Multiple Bernoulli (MB) Language Model has been generally considered too computationally expensive for practical purposes and superseded by the more efficient multinomial approach. While, the model has many attractive properties, little is actually known about the retrieval effectiveness of the MB model due to its high cost of execution. In this paper, we show how an efficient implementation of this model can be achieved. The resulting method is comparable in terms of efficiency to other standard term matching algorithms (such as the vector space model, BM25 and the multinomial Language Model). [ABSTRACT FROM AUTHOR]

Recent years have seen the development of different modalities for video retrieval. The most common of these are (1) to use text from speech recognition or closed captions, (2) to match keyframes using image retrieval techniques like colour and texture [6] and (3) to use semantic features like "indoor", "outdoor" or "persons". Of these, text-based retrieval is the most mature and useful, while image-based retrieval using low-level image features usually depends on matching keyframes rather than whole-shots. Automatic detection of video concepts is receiving much attention and as progress is made in this area we will see consequent impact on the quality of video retrieval. In practice it is the combination of these techniques which realises the most useful, and effective, video retrieval as shown by us repeatedly in TRECVid [5]. [ABSTRACT FROM AUTHOR]

As bearer of high-level semantics, audio signal is being more and more used in content-based multimedia retrieval. In this paper, we investigate TV tennis game highlight detection based on the use of both short and long term audio features and propose two approaches, decision fusion and hierarchical classifier, in order to combine these two kinds of audio features. As more information is included in decision making, the overall performance of the system is enhanced. [ABSTRACT FROM AUTHOR]

Although it has been shown that in test collection based studies, stemming improves retrieval effectiveness in an information retrieval system, morphological variations of queries searching on the same topic are less well understood. This work examines the broad morphological variation that searchers of an Arabic retrieval system put into their queries. In this study, 15 native Arabic speakers were asked to generate queries, morphological variants of query words were collated across users. Queries composed of either the commonest or rarest variants of each word were submitted to a retrieval system and the effectiveness of the searches was measured. It was found that queries composed of the more popular morphological variants were more likely to retrieve relevant documents that those composed of less popular. [ABSTRACT FROM AUTHOR]

Improving the accuracy of assigning new email messages to small folders can reduce the likelihood of users creating duplicate folders for some topics. In this paper we presented a hybrid classification model, PERC, and use the Enron Email Corpus to investigate the performance of kNN, SVM and PERC in a simulation of a real-time situation. Our results show that PERC is significantly better at assigning messages to small folders. The effects of different parameter settings for the classifiers are discussed. [ABSTRACT FROM AUTHOR]

The purpose of contextual information retrieval is to make some exploration towards designing user specific search engines that are able to adapt the retrieval model to the variety of differences on user's contexts. In this paper we propose an influence diagram based retrieval model which is able to incorporate contexts, viewed as user's long-term interests into the retrieval process. Keywords: personalized information access, influence diagrams, user context. [ABSTRACT FROM AUTHOR]

This paper presents an experimental study on the effect of topic familiarity on the assessment behaviour of online searchers. In particular we investigate the effect of topic familiarity on the resources and relevance criteria used by searchers. Our results indicate that searching on an unfamiliar topic leads to use of more generic and fewer specialised resources and that searchers employ different relevance criteria when searching on less familiar topics. [ABSTRACT FROM AUTHOR]

Genomic IR, characterized by its highly specific information need, severe synonym and polysemy problem, long term name and rapid growing literature size, is challenging IR community. In this paper, we are focused on addressing the synonym and polysemy issue within the language model framework. Unlike the ways translation model and traditional query expansion techniques approach this issue, we incorporate concept-based indexing into a basic language model for genomic IR. In particular, we adopt UMLS concepts as indexing and searching terms. A UMLS concept stands for a unique meaning in the biomedicine domain; a set of synonymous terms will share same concept ID. Therefore, the new approach makes the document ranking effective while maintaining the simplicity of language models. A comparative experiment on the TREC 2004 Genomics Track data shows significant improvements are obtained by incorporating concept-based indexing into a basic language model. The MAP (mean average precision) is significantly raised from 29.17% (the baseline system) to 36.94%. The performance of the new approach is also significantly superior to the mean (21.72%) of official runs participated in TREC 2004 Genomics Track and is comparable to the performance of the best run (40.75%). Most official runs including the best run extensively use various query expansion and pseudo-relevance feedback techniques while our approach does nothing except for the incorporation of concept-based indexing, which evidences the view that semantic smoothing, i.e. the incorporation of synonym and sense information into the language models, is a more standard approach to achieving the effects traditional query expansion and pseudo-relevance feedback techniques target. [ABSTRACT FROM AUTHOR]

Parallel corpora are a valuable resource for tasks such as cross-language information retrieval and data-driven natural language processing systems. Previously only small scale corpora have been available, thus restricting their practical use. This paper describes a system that overcomes this limitation by automatically collecting high quality parallel bilingual corpora from the web. Previous systems used a single principle feature for parallel web page verification, whereas we use multiple features to identify parallel texts via a k-nearest-neighbor classifier. Our system was evaluated using a data set containing 6500 Chinese-English candidate parallel pairs that have been manually annotated. Experiments show that the use of a k-nearest-neighbors classifier with multiple features achieves substantial improvements over the systems that use any one of these features. The system achieved a precision rate of 95% and a recall rate of 97%, and thus is a significant improvement over earlier work. [ABSTRACT FROM AUTHOR]

Detection of highly similar sequences within genomic collections has a number of applications, including the assembly of expressed sequence tag data, genome comparison, and clustering sequence collections for improved search speed and accuracy. While several approaches exist for this task, they are becoming infeasible — either in space or in time — as genomic collections continue to grow at a rapid pace. In this paper we present an approach based on document fingerprinting for identifying highly similar sequences. Our approach uses a modest amount of memory and executes in a time roughly proportional to the size of the collection. We demonstrate substantial speed improvements compared to the CD-HIT algorithm, the most successful existing approach for clustering large protein sequence collections. [ABSTRACT FROM AUTHOR]

Our cultural heritage, as preserved in libraries, archives and museums, is made up of documents written many centuries ago. Large-scale digitization initiatives make these documents available to non-expert users through digital libraries and vertical search engines. For a user, querying a historic document collection may be a disappointing experience: queries involving modern words may not be very effective for retrieving documents that contain many historic terms. We propose a cross-language approach to historic document retrieval, and investigate (1) the automatic construction of translation resources for historic languages, and (2) the retrieval of historic documents using cross-language information retrieval techniques. Our experimental evidence is based on a collection of 17th century Dutch documents and a set of 25 known-item topics in modern Dutch. Our main findings are as follows: First, we are able to automatically construct rules for modernizing historic language based on comparing (a) phonetic sequence similarity, (b) the relative frequency of consonant and vowel sequences, and (c) the relative frequency of character n-gram sequences, of historic and modern corpora. Second, modern queries are not very effective for retrieving historic documents, but the historic language tools lead to a substantial improvement in retrieval effectiveness. The improvements are above and beyond the improvement due to using a modern stemming algorithm (whose effectiveness actually goes up when the historic language is modernized). [ABSTRACT FROM AUTHOR]

The quality of search engines depends usually on the content of the returned documents rather than on the text used to express this content. So ideally, search techniques should be directed more toward the semantic dependencies underlying documents than toward the texts themselves. The most visible examples in this direction are Latent Semantic Analysis (LSA), and the Hyperspace Analog to Language (HAL). If these techniques are really based on semantic dependencies, as they contend, then they should be applicable across languages. To investigate this contention we used electronic versions of two kinds of material with their translations: a novel, and a popular treatise about cosmology. We used the analogy of fingerprinting as employed in forensics to establish whether individuals are related. Genetic fingerprinting uses enzymes to split the DNA and then compare the resulting band patterns. Likewise, in our research we used queries to split a document into fragments. If a search technique really isolates fragments semantically related to the query, then a document and its translation should have similar band patterns. In this paper we (1) present the fingerprinting technique, (2) introduce the material used, and (3) report results of an evaluation for two semantic indexing techniques. [ABSTRACT FROM AUTHOR]

We have proposed a novel image retrieval system that incorporates a workspace where users can organise their search results. A task-oriented and user-centred experiment has been devised involving design professionals and several types of realistic search tasks. We study the workspace's effect on two aspects: task conceptualisation and query formulation. A traditional relevance feedback system serves as baseline. The results of this study show that the workspace is more useful with respect to both of the above aspects. The proposed approach leads to a more effective and enjoyable search experience. [ABSTRACT FROM AUTHOR]

Motion capturing has become an important tool in fields such as sports sciences, biometrics, and particularly in computer animation, where large collections of motion material are accumulated in the production process. In order to fully exploit motion databases for reuse and for the synthesis of new motions, one needs efficient retrieval and browsing methods to identify similar motions. So far, only ad-hoc methods for content-based motion retrieval have been proposed, which lack efficiency and rely on quantitative, numerical similarity measures, making it difficult to identify logically related motions. We propose an efficient motion retrieval system based on the query-by-example paradigm, which employs qualitative, geometric similarity measures. This allows for intuitive and interactive browsing in a purely content-based fashion without relying on textual annotations. We have incorporated this technology in a novel user interface facilitating query formulation as well as visualization and ranking of search results. [ABSTRACT FROM AUTHOR]

In this paper we consider episodic memory for system design in image retrieval. Time and location are the main factors in episodic memory, and these types of data were combined for image event clustering. We conducted a user studies to compare five image browsing systems using searching time and user satisfaction as criteria for success. Our results showed that the browser which clusters images based on time and location data combined was significantly better than four other more standard browsers. This suggests that episodic memory is potentially useful for improving personal image management. [ABSTRACT FROM AUTHOR]

The aim of our research is to produce and assess short summaries to aid users' relevance judgements, for example for a search engine result page. In this paper we present our new metric for measuring summary quality based on representativeness and judgeability, and compare the summary quality of our system to that of Google. We discuss the basis for constructing our evaluation methodology in contrast to previous relevant open evaluations, arguing that the elements which make up an evaluation methodology: the tasks, data and metrics, are interdependent and the way in which they are combined is critical to the effectiveness of the methodology. The paper discusses the relationship between these three factors as implemented in our own work, as well as in SUMMAC/MUC/DUC. [ABSTRACT FROM AUTHOR]

When presented with a retrieved document, users of a search engine are usually left with the task of pinning down the relevant information inside the document. Often this is done by a time-consuming combination of skimming, scrolling and Ctrl+F. In the setting of a digital library for scientific literature the issue is especially urgent when dealing with reference works, such as surveys and handbooks, as these typically contain long documents. Our aim is to develop methods for providing a "go-read-here" type of retrieval functionality, which points the user to a segment where she can best start reading to find out about her topic of interest. We examine multiple query-independent ways of segmenting texts into coherent chunks that can be returned in response to a query. Most (experienced) authors use paragraph breaks to indicate topic shifts, thus providing us with one way of segmenting documents. We compare this structural method with semantic text segmentation methods, both with respect to topical focus and relevancy. Our experimental evidence is based on manually segmented scientific documents and a set of queries against this corpus. Structural segmentation based on contiguous blocks of relevant paragraphs is shown to be a viable solution for our intended application of providing "go-read-here" functionality. [ABSTRACT FROM AUTHOR]