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1.
Evaluating the effectiveness of content-oriented XML retrieval methods   总被引:1,自引:0,他引:1  
Content-oriented XML retrieval approaches aim at a more focused retrieval strategy: Instead of retrieving whole documents, document components that are exhaustive to the information need while at the same time being as specific as possible should be retrieved. In this article, we show that the evaluation methods developed for standard retrieval must be modified in order to deal with the structure of XML documents. More precisely, the size and overlap of document components must be taken into account. For this purpose, we propose a new effectiveness metric based on the definition of a concept space defined upon the notions of exhaustiveness and specificity of a search result. We compare the results of this new metric by the results obtained with the official metric used in INEX, the evaluation initiative for content-oriented XML retrieval.
Gabriella KazaiEmail:
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2.
Sentence level novelty detection aims at spotting sentences with novel information from an ordered sentence list. In the task, sentences appearing later in the list with no new meanings are eliminated. For the task of novelty detection, the contributions of this paper are three-fold. First, conceptually, this paper reveals the computational nature of the task currently overlooked by the Novelty community—Novelty as a combination of partial overlap (PO) and complete overlap (CO) relations between sentences. We define partial overlap between two sentences as a sharing of common facts, while complete overlap is when one sentence covers all of the meanings of the other sentence. Second, technically, a novel approach, the selected pool method is provided which follows naturally from the PO-CO computational structure. We provide formal error analysis for selected pool and methods based on this PO-CO framework. We address the question how accurate must the PO judgments be to outperform the baseline pool method. Third, experimentally, results were presented for all the three novelty datasets currently available. Results show that the selected pool is significantly better or no worse than the current methods, an indication that the term overlap criterion for the PO judgments could be adequately accurate.
Shaoping MaEmail:
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3.
Smoothing of document language models is critical in language modeling approaches to information retrieval. In this paper, we present a novel way of smoothing document language models based on propagating term counts probabilistically in a graph of documents. A key difference between our approach and previous approaches is that our smoothing algorithm can iteratively propagate counts and achieve smoothing with remotely related documents. Evaluation results on several TREC data sets show that the proposed method significantly outperforms the simple collection-based smoothing method. Compared with those other smoothing methods that also exploit local corpus structures, our method is especially effective in improving precision in top-ranked documents through “filling in” missing query terms in relevant documents, which is attractive since most users only pay attention to the top-ranked documents in search engine applications.
ChengXiang ZhaiEmail:
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4.
In recent years graph-ranking based algorithms have been proposed for single document summarization and generic multi-document summarization. The algorithms make use of the “votings” or “recommendations” between sentences to evaluate the importance of the sentences in the documents. This study aims to differentiate the cross-document and within-document relationships between sentences for generic multi-document summarization and adapt the graph-ranking based algorithm for topic-focused summarization. The contributions of this study are two-fold: (1) For generic multi-document summarization, we apply the graph-based ranking algorithm based on each kind of sentence relationship and explore their relative importance for summarization performance. (2) For topic-focused multi-document summarization, we propose to integrate the relevance of the sentences to the specified topic into the graph-ranking based method. Each individual kind of sentence relationship is also differentiated and investigated in the algorithm. Experimental results on DUC 2002–DUC 2005 data demonstrate the great importance of the cross-document relationships between sentences for both generic and topic-focused multi-document summarizations. Even the approach based only on the cross-document relationships can perform better than or at least as well as the approaches based on both kinds of relationships between sentences.
Xiaojun WanEmail:
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5.
We adapt the cluster hypothesis for score-based information retrieval by claiming that closely related documents should have similar scores. Given a retrieval from an arbitrary system, we describe an algorithm which directly optimizes this objective by adjusting retrieval scores so that topically related documents receive similar scores. We refer to this process as score regularization. Because score regularization operates on retrieval scores, regardless of their origin, we can apply the technique to arbitrary initial retrieval rankings. Document rankings derived from regularized scores, when compared to rankings derived from un-regularized scores, consistently and significantly result in improved performance given a variety of baseline retrieval algorithms. We also present several proofs demonstrating that regularization generalizes methods such as pseudo-relevance feedback, document expansion, and cluster-based retrieval. Because of these strong empirical and theoretical results, we argue for the adoption of score regularization as general design principle or post-processing step for information retrieval systems.
Fernando DiazEmail:
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6.
In this paper, we propose a new term dependence model for information retrieval, which is based on a theoretical framework using Markov random fields. We assume two types of dependencies of terms given in a query: (i) long-range dependencies that may appear for instance within a passage or a sentence in a target document, and (ii) short-range dependencies that may appear for instance within a compound word in a target document. Based on this assumption, our two-stage term dependence model captures both long-range and short-range term dependencies differently, when more than one compound word appear in a query. We also investigate how query structuring with term dependence can improve the performance of query expansion using a relevance model. The relevance model is constructed using the retrieval results of the structured query with term dependence to expand the query. We show that our term dependence model works well, particularly when using query structuring with compound words, through experiments using a 100-gigabyte test collection of web documents mostly written in Japanese. We also show that the performance of the relevance model can be significantly improved by using the structured query with our term dependence model.
Koji EguchiEmail:
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7.
To obtain high precision at top ranks by a search performed in response to a query, researchers have proposed a cluster-based re-ranking paradigm: clustering an initial list of documents that are the most highly ranked by some initial search, and using information induced from these (often called) query-specific clusters for re-ranking the list. However, results concerning the effectiveness of various automatic cluster-based re-ranking methods have been inconclusive. We show that using query-specific clusters for automatic re-ranking of top-retrieved documents is effective with several methods in which clusters play different roles, among which is the smoothing of document language models. We do so by adapting previously-proposed cluster-based retrieval approaches, which are based on (static) query-independent clusters for ranking all documents in a corpus, to the re-ranking setting wherein clusters are query-specific. The best performing method that we develop outperforms both the initial document-based ranking and some previously proposed cluster-based re-ranking approaches; furthermore, this algorithm consistently outperforms a state-of-the-art pseudo-feedback-based approach. In further exploration we study the performance of cluster-based smoothing methods for re-ranking with various (soft and hard) clustering algorithms, and demonstrate the importance of clusters in providing context from the initial list through a comparison to using single documents to this end.
Oren KurlandEmail:
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8.
Precision prediction based on ranked list coherence   总被引:1,自引:0,他引:1  
We introduce a statistical measure of the coherence of a list of documents called the clarity score. Starting with a document list ranked by the query-likelihood retrieval model, we demonstrate the score's relationship to query ambiguity with respect to the collection. We also show that the clarity score is correlated with the average precision of a query and lay the groundwork for useful predictions by discussing a method of setting decision thresholds automatically. We then show that passage-based clarity scores correlate with average-precision measures of ranked lists of passages, where a passage is judged relevant if it contains correct answer text, which extends the basic method to passage-based systems. Next, we introduce variants of document-based clarity scores to improve the robustness, applicability, and predictive ability of clarity scores. In particular, we introduce the ranked list clarity score that can be computed with only a ranked list of documents, and the weighted clarity score where query terms contribute more than other terms. Finally, we show an approach to predicting queries that perform poorly on query expansion that uses techniques expanding on the ideas presented earlier.
W. Bruce CroftEmail:
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9.
Modeling context through domain ontologies   总被引:1,自引:0,他引:1  
Traditional information retrieval systems aim at satisfying most users for most of their searches, leaving aside the context in which the search takes place. We propose to model two main aspects of context: The themes of the user's information need and the specific data the user is looking for to achieve the task that has motivated his search. Both aspects are modeled by means of ontologies. Documents are semantically indexed according to the context representation and the user accesses information by browsing the ontologies. The model has been applied to a case study that has shown the added value of such a semantic representation of context.
Daniel EgretEmail:
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10.
Negation recognition in medical narrative reports   总被引:1,自引:0,他引:1  
Substantial medical data, such as discharge summaries and operative reports are stored in electronic textual form. Databases containing free-text clinical narratives reports often need to be retrieved to find relevant information for clinical and research purposes. The context of negation, a negative finding, is of special importance, since many of the most frequently described findings are such. When searching free-text narratives for patients with a certain medical condition, if negation is not taken into account, many of the documents retrieved will be irrelevant. Hence, negation is a major source of poor precision in medical information retrieval systems. Previous research has shown that negated findings may be difficult to identify if the words implying negations (negation signals) are more than a few words away from them. We present a new pattern learning method for automatic identification of negative context in clinical narratives reports. We compare the new algorithm to previous methods proposed for the same task, and show its advantages: accuracy improvement compared to other machine learning methods, and much faster than manual knowledge engineering techniques with matching accuracy. The new algorithm can be applied also to further context identification and information extraction tasks.
Lior RokachEmail:
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11.
With increasingly higher numbers of non-English language web searchers the problems of efficient handling of non-English Web documents and user queries are becoming major issues for search engines. The main aim of this review paper is to make researchers aware of the existing problems in monolingual non-English Web retrieval by providing an overview of open issues. A significant number of papers are reviewed and the research issues investigated in these studies are categorized in order to identify the research questions and solutions proposed in these papers. Further research is proposed at the end of each section.
Efthimis N. EfthimiadisEmail:
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12.
Deciphering the diplomatic archives of fifteenth-century Italy   总被引:1,自引:1,他引:0  
This article examines the repercussions of the explosion of paper documents generated by new developments in diplomatic practice in Italian city-states between 1450 and 1500. With the proliferation of resident ambassadors whose daily duties centered around writing and receiving letters and other documents, a flood of written material was produced. The management and archiving of all this material triggered the formation of new institutions, of new methods of working, and of new personnel. Though the results of the efforts at archiving were often fitful and incomplete, the governments of the Italian peninsula henceforth sought to collect, control and preserve diplomatic documents so that they could be referenced in the future.
Paul Marcus DoverEmail:

Paul M. Dover   is Assistant Professor of History at Kennesaw State University in Kennesaw, Georgia. He has published several articles on the political and intellectual history of Renaissance Italy. He is currently writing a book on ambassadors and the culture of diplomacy in fifteenth-century Italy. He holds a PhD from Yale University.  相似文献   

13.
This paper gives an overview of the archival issues that relate to digitally signed documents. First, by way of introduction, the advanced digital signature is presented briefly. In the second part, a number of problems are discussed that present themselves when a digital signature is used as a proof of authenticity and integrity for digital documents in general. In particular, it is also being investigated whether it makes any sense for the archivist to digitally sign all electronic records under his or her management. Problems relating to the (medium) long-term archiving of digitally signed documents are dealt with in the third part. After an overview of the sticking points for long-term validation (“Archival issues”) a number of possible solutions are discussed (“Solutions for long-term archiving”).
Filip BoudrezEmail:
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14.
A review and analysis of the rules and regulations including the tax aspects of making an investment in India is presented. The full range from Foreign Direct Investment to different forms of doing business with specific examples from the publishing industry is explored to help understand current policies and regulations.
Sandeep ChauflaEmail: Email:
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15.
To put an end to the large copyright trade deficit, both Chinese government agencies and publishing houses have been striving for entering the international publication market. The article analyzes the background of the going-global strategy, and sums up the performance of both Chinese administrations and publishers.
Qing Fang (Corresponding author)Email:
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16.
Modern retrieval test collections are built through a process called pooling in which only a sample of the entire document set is judged for each topic. The idea behind pooling is to find enough relevant documents such that when unjudged documents are assumed to be nonrelevant the resulting judgment set is sufficiently complete and unbiased. Yet a constant-size pool represents an increasingly small percentage of the document set as document sets grow larger, and at some point the assumption of approximately complete judgments must become invalid. This paper shows that the judgment sets produced by traditional pooling when the pools are too small relative to the total document set size can be biased in that they favor relevant documents that contain topic title words. This phenomenon is wholly dependent on the collection size and does not depend on the number of relevant documents for a given topic. We show that the AQUAINT test collection constructed in the recent TREC 2005 workshop exhibits this biased relevance set; it is likely that the test collections based on the much larger GOV2 document set also exhibit the bias. The paper concludes with suggested modifications to traditional pooling and evaluation methodology that may allow very large reusable test collections to be built.
Ellen VoorheesEmail:
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17.
This article examines the archival methods developed by Colbert to train his son in state administration. Based on Colbert’s correspondence with his son, it reveals the practices Colbert thought necessary to collect and manage information in his state encyclopedic archive during the last half of the 17th century.
Jacob SollEmail:
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18.
This article analyzes current industry practices toward the identification of digital book content. It highlights key technology trends, workflow considerations and supply chain behaviors, and examines the implications of these trends and behaviors on the production, discoverability, purchasing and consumption of digital book products.
Andy WeissbergEmail:
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19.
A summary overview of the children’s and young adult publishing industry in China with a focus on the size of the market, ten major publishing houses, copyright and trends. Special emphasis has been placed on specific transaction for the sale of translation rights from German language publishers to China and minimal activities of German rights sold to Chinese publishers.
Jing BartzEmail:
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20.
There is a wide set of evaluation metrics available to compare the quality of text clustering algorithms. In this article, we define a few intuitive formal constraints on such metrics which shed light on which aspects of the quality of a clustering are captured by different metric families. These formal constraints are validated in an experiment involving human assessments, and compared with other constraints proposed in the literature. Our analysis of a wide range of metrics shows that only BCubed satisfies all formal constraints. We also extend the analysis to the problem of overlapping clustering, where items can simultaneously belong to more than one cluster. As Bcubed cannot be directly applied to this task, we propose a modified version of Bcubed that avoids the problems found with other metrics.
Felisa VerdejoEmail:
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