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Abstract

Whereas in language words of high frequency are generally associated with low content [Bookstein, A., & Swanson, D. (1974). Probabilistic models for automatic indexing. Journal of the American Society of Information Science, 25(5), 312-318; Damerau, F. J. (1965). An experiment in automatic indexing. American Documentation, 16, 283-289; Harter, S. P. (1974). A probabilistic approach to automatic keyword indexing. PhD thesis, University of Chicago; Sparck-Jones, K. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28, 11-21; Yu, C., & Salton, G. (1976). Precision weighting - an effective automatic indexing method. Journal of the Association for Computer Machinery (ACM), 23(1), 76-88], shallow syntactic fragments of high frequency generally correspond to lexical fragments of high content [Lioma, C., & Ounis, I. (2006). Examining the content load of part of speech blocks for information retrieval. In Proceedings of the international committee on computational linguistics and the association for computational linguistics (COLING/ACL 2006), Sydney, Australia]. We implement this finding to Information Retrieval, as follows. We present a novel automatic query reformulation technique, which is based on shallow syntactic evidence induced from various language samples, and used to enhance the performance of an Information Retrieval system. Firstly, we draw shallow syntactic evidence from language samples of varying size, and compare the effect of language sample size upon retrieval performance, when using our syntactically-based query reformulation (SQR) technique. Secondly, we compare SQR to a state-of-the-art probabilistic pseudo-relevance feedback technique. Additionally, we combine both techniques and evaluate their compatibility. We evaluate our proposed technique across two standard Text REtrieval Conference (TREC) English test collections, and three statistically different weighting models. Experimental results suggest that SQR markedly enhances retrieval performance, and is at least comparable to pseudo-relevance feedback. Notably, the combination of SQR and pseudo-relevance feedback further enhances retrieval performance considerably. These collective experimental results confirm the tenet that high frequency shallow syntactic fragments correspond to content-bearing lexical fragments.

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Date

12. 6.2008 20:18:22

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Abstract

Making the Nation Safer: The Role of Science and Technology in Countering Terrorism The commitment of the scientific, engineering, and health communities to helping the United States and the world respond to security challenges became evident after September 11, 2001. The U.S. National Research Council's report an "Making the Nation Safer: The Role of Science and Technology in Countering Terrorism," (National Research Council, 2002, p. 1) explains the context of such a new commitment: Terrorism is a serious threat to the Security of the United States and indeed the world. The vulnerability of societies to terrorist attacks results in part from the proliferation of chemical, biological, and nuclear weapons of mass destruction, but it also is a consequence of the highly efficient and interconnected systems that we rely an for key services such as transportation, information, energy, and health care. The efficient functioning of these systems reflects great technological achievements of the past century, but interconnectedness within and across systems also means that infrastructures are vulnerable to local disruptions, which could lead to widespread or catastrophic failures. As terrorists seek to exploit these vulnerabilities, it is fitting that we harness the nation's exceptional scientific and technological capabilities to Counter terrorist threats. A committee of 24 of the leading scientific, engineering, medical, and policy experts in the United States conducted the study described in the report. Eight panels were separately appointed and asked to provide input to the committee. The panels included: (a) biological sciences, (b) chemical issues, (c) nuclear and radiological issues, (d) information technology, (e) transportation, (f) energy facilities, Cities, and fixed infrastructure, (g) behavioral, social, and institutional issues, and (h) systems analysis and systems engineering. The focus of the committee's work was to make the nation safer from emerging terrorist threats that sought to inflict catastrophic damage an the nation's people, its infrastructure, or its economy. The committee considered nine areas, each of which is discussed in a separate chapter in the report: nuclear and radiological materials, human and agricultural health systems, toxic chemicals and explosive materials, information technology, energy systems, transportation systems, Cities and fixed infrastructure, the response of people to terrorism, and complex and interdependent systems. The chapter an information technology (IT) is particularly relevant to this special issue. The report recommends that "a strategic long-term research and development agenda should be established to address three primary counterterrorismrelated areas in IT: information and network security, the IT needs of emergency responders, and information fusion and management" (National Research Council, 2002, pp. 11 -12). The MD in information and network security should include approaches and architectures for prevention, identification, and containment of cyber-intrusions and recovery from them. The R&D to address IT needs of emergency responders should include ensuring interoperability, maintaining and expanding communications capability during an emergency, communicating with the public during an emergency, and providing support for decision makers. The R&D in information fusion and management for the intelligence, law enforcement, and emergency response communities should include data mining, data integration, language technologies, and processing of image and audio data. Much of the research reported in this special issue is related to information fusion and management for homeland security.

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Abstract

Shannon provides a model whereby an information source selects a desired message, out of a set of possible messages, that is then formed into a signal. The signal is sent over the communication channel to a receiver, which then transforms the signal back to a message that is relayed to its destination (Shannon & Weaver, 1949/1963, p. 7). Problems connected with this model have remained with us. Some of the concepts are ambiguous; the identification of information with a process has spancelled the debate; the problems of measuring the amount of information, the relation of information to meaning, and questions about the truth value of information have remained. Balancing attention between the process and the act of receiving information, and deterrnining the character of the receiver, has also been the focus of work and debate. Information science has mined work from other disciplines involving information theory and has also produced its own theory. The desire for theory remains (Hjorland, 1998; Saracevic, 1999), but what theory will deliver is unclear. The distinction between data and information, or communication and information, is not of concern here. The convention that data, at some point of use, become information, and that information is transferred in a process of communication suffices for this discussion. Substitution of any of these terms is not a problem. More problematic is the relationship between information and knowledge. It seems accepted that at some point the data by perception, or selection, become information, which feeds and alters knowledge structures in a human recipient. What that process of alteration is, and its implications, remain problematic. This review considers the following questions: 1. What can be gleaned from the history of reviews of information in information science? 2. What current maps, guides, and surveys are available to elaborate our understanding of the issues? 3. Is there a parallel development of work outside information science an information theory of use to us? 4. Is there a dominant view of information within information science? 5. What can we say about issues like measurement, meaning, and misinformation? 6. Is there other current work of relevance that can assist attempts, in information science, to develop a theory of information?

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Abstract

The impetus for this essay is the library community's uncertainty regarding the present and future direction of the library catalog in the era of Google and mass digitization projects. The uncertainty is evident at the highest levels. Deanna Marcum, Associate Librarian for Library Services at the Library of Congress (LC), is struck by undergraduate students who favor digital resources over the online library catalog because such resources are available at anytime and from anywhere (Marcum, 2006). She suggests that "the detailed attention that we have been paying to descriptive cataloging may no longer be justified ... retooled catalogers could give more time to authority control, subject analysis, [and] resource identification and evaluation" (Marcum, 2006, 8). In an abrupt about-face, LC terminated series added entries in cataloging records, one of the few subject-rich fields in such records (Cataloging Policy and Support Office, 2006). Mann (2006b) and Schniderman (2006) cite evidence of LC's prevailing viewpoint in favor of simplifying cataloging at the expense of subject cataloging. LC commissioned Karen Calhoun (2006) to prepare a report on "revitalizing" the online library catalog. Calhoun's directive is clear: divert resources from cataloging mass-produced formats (e.g., books) to cataloging the unique primary sources (e.g., archives, special collections, teaching objects, research by-products). She sums up her rationale for such a directive, "The existing local catalog's market position has eroded to the point where there is real concern for its ability to weather the competition for information seekers' attention" (p. 10). At the University of California Libraries (2005), a task force's recommendations parallel those in Calhoun report especially regarding the elimination of subject headings in favor of automatically generated metadata. Contemplating these events prompted me to revisit the glorious past of the online library catalog. For a decade and a half beginning in the early 1980s, the online library catalog was the jewel in the crown when people eagerly queued at its terminals to find information written by the world's experts. I despair how eagerly people now embrace Google because of the suspect provenance of the information Google retrieves. Long ago, we could have added more value to the online library catalog but the only thing we changed was the catalog's medium. Our failure to act back then cost the online catalog the crown. Now that the era of mass digitization has begun, we have a second chance at redesigning the online library catalog, getting it right, coaxing back old users, and attracting new ones. Let's revisit the past, reconsidering missed opportunities, reassessing their merits, combining them with new directions, making bold decisions and acting decisively on them.

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Abstract

The concept of c-space is proposed as a visualization schema relating containers of content to cataloging surrogates and classification structures. Possible applications of keyword vector clusters within c-space could include improved retrieval rates through the use of captioning within visual hierarchies, tracings of semantic bleeding among subclasses, and access to buried knowledge within subject-neutral publication containers. The Scholastica Project is described as one example, following a tradition of research dating back to the 1980's. Preliminary focus group assessment indicates that this type of classification rendering may offer digital library searchers enriched entry strategies and an expanded range of re-entry vocabularies. Those of us who work in traditional libraries typically assume that our systems of classification: Library of Congress Classification (LCC) and Dewey Decimal Classification (DDC), are descriptive rather than prescriptive. In other words, LCC classes and subclasses approximate natural groupings of texts that reflect an underlying order of knowledge, rather than arbitrary categories prescribed by librarians to facilitate efficient shelving. Philosophical support for this assumption has traditionally been found in a number of places, from the archetypal tree of knowledge, to Aristotelian categories, to the concept of discursive formations proposed by Michel Foucault. Gary P. Radford has elegantly described an encounter with Foucault's discursive formations in the traditional library setting: "Just by looking at the titles on the spines, you can see how the books cluster together...You can identify those books that seem to form the heart of the discursive formation and those books that reside on the margins. Moving along the shelves, you see those books that tend to bleed over into other classifications and that straddle multiple discursive formations. You can physically and sensually experience...those points that feel like state borders or national boundaries, those points where one subject ends and another begins, or those magical places where one subject has morphed into another..."

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Content

Vgl.: http://findarticles.com/p/articles/mi_m0FWE/is_3_7/ai_99011617.

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Content

Librarianship incorporates the tools of knowledge organization as part of its role as cultural disseminator. Subject headings and classification were both intended by their 19`h century promulgators - perhaps most notably Dewey and Cutter - to facilitate learning by grouping materials of high quality together. We might call this enhanced serendipity if we think it happens by accident or act of fate, or we might call it curatorship if we realize the responsibility inherent in our social role. The cataloger's job always has been to place each work sensitively among other works related to it, and to make the relationships explicit to facilitate and even encourage selection (see Miksa 1983). Schallier (2004) reported on the use of classification in an online catalog to enhance just such a curatorial purpose. UDC classification codes were exploded into linguistic strings to allow users to search, not just for a given term, but for the terms that occur around it - that is, terms that are adjacent in the classification. These displays are used alongside LCSH to provide enhanced-serendipity for users. What caught my attention was the intention of the project (p. 271): UDC permits librarians to build virtual library shelves, where a document's subjects can be described in thematic categories rather than in detailed verbal terms. And: It is our experience that most end users are not familiar with large controlled vocabularies. UDC could be an answer to this, since its alphanumeric makeup could be used to build a tree structure of terms, which would guide end users in their searchers. There are other implications from this project, including background linkage from UDC codes that drive the "virtual shelves" to subject terms that drive the initial classification. Knowledge organization has consequences in both theory and application."

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Abstract

Consider a sentence such as "the current price of tea in China is 35 cents per pound." In a library with millions of books we might find many statements of the above form that we could capture today with relatively simple rules: rather than pursuing every variation of a statement, programs can wait, like predators at a water hole, for their informational prey to reappear in a standard linguistic pattern. We can make inferences from sentences such as "NAME1 born at NAME2 in DATE" that NAME more likely than not represents a person and NAME a place and then convert the statement into a proposition about a person born at a given place and time. The changing price of tea in China, pedestrian birth and death dates, or other basic statements may not be truth and beauty in the Phaedrus, but a digital library that could plot the prices of various commodities in different markets over time, plot the various lifetimes of individuals, or extract and classify many events would be very useful. Services such as the Syllabus Finder1 and H-Bot2 (which Dan Cohen describes elsewhere in this issue of D-Lib) represent examples of information extraction already in use. H-Bot, in particular, builds on our evolving ability to extract information from very large corpora such as the billions of web pages available through the Google API. Aside from identifying higher order statements, however, users also want to search and browse named entities: they want to read about "C. P. E. Bach" rather than his father "Johann Sebastian" or about "Cambridge, Maryland", without hearing about "Cambridge, Massachusetts", Cambridge in the UK or any of the other Cambridges scattered around the world. Named entity identification is a well-established area with an ongoing literature. The Natural Language Processing Research Group at the University of Sheffield has developed its open source Generalized Architecture for Text Engineering (GATE) for years, while IBM's Unstructured Information Analysis and Search (UIMA) is "available as open source software to provide a common foundation for industry and academia." Powerful tools are thus freely available and more demanding users can draw upon published literature to develop their own systems. Major search engines such as Google and Yahoo also integrate increasingly sophisticated tools to categorize and identify places. The software resources are rich and expanding. The reference works on which these systems depend, however, are ill-suited for historical analysis. First, simple gazetteers and similar authority lists quickly grow too big for useful information extraction. They provide us with potential entities against which to match textual references, but existing electronic reference works assume that human readers can use their knowledge of geography and of the immediate context to pick the right Boston from the Bostons in the Getty Thesaurus of Geographic Names (TGN), but, with the crucial exception of geographic location, the TGN records do not provide any machine readable clues: we cannot tell which Bostons are large or small. If we are analyzing a document published in 1818, we cannot filter out those places that did not yet exist or that had different names: "Jefferson Davis" is not the name of a parish in Louisiana (tgn,2000880) or a county in Mississippi (tgn,2001118) until after the Civil War.