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  • × theme_ss:"Computerlinguistik"
  • × type_ss:"a"
  1. Rolland, M.T.: Sachgebietsstandardisierung im Bereich der Informationswissenschaft (1996) 0.04 
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    Source
    Die digitale Revolution: Deutscher Dokumentartag 1996, Neue Universität Heidelberg, 24.-26.9.1996. Hrsg.: W. Neubauer
  2. dpa: 14 Forscher mit viel Geld angelockt : Wolfgang-Paul-Preis (2001) 0.02 
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    Content
    Darin. "Die Sprachwissenschaftlerin Christiane Fellbaum (dpa-Bild) wird ihr Preisgeld für das an der Berlin-Brandenburgischen Akademie der Wissenschaften zu erstellende "Digitale Wörterbuch der Deutschen Sprache des 20. Jahrhunderts" einsetzen. Sie setzt mit ihrem Computer dort an, wo konventionelle Wörterbücher nicht mehr mithalten können. Sie stellt per Knopfdruck Wortverbindungen her, die eine Sprache so reich an Bildern und Vorstellungen - und damit einzigartig - machen. Ihr elektronisches Lexikon aus über 500 Millionen Wörtern soll später als Datenbank zugänglich sein. Seine Grundlage ist die deutsche Sprache der vergangenen hundert Jahre - ein repräsentativer Querschnitt, zusammengestellt aus Literatur, Zeitungsdeutsch, Fachbuchsprache, Werbetexten und niedergeschriebener Umgangssprache. Wo ein Wörterbuch heute nur ein Wort mit Synonymen oder wenigen Verwendungsmöglichkeiten präsentiert, spannt die Forscherin ein riesiges Netz von Wortverbindungen. Bei Christiane Fellbaums Systematik heißt es beispielsweise nicht nur "verlieren", sondern auch noch "den Faden" oder "die Geduld" verlieren - samt allen möglichen weiteren Kombinationen, die der Computer wie eine Suchmaschine in seinen gespeicherten Texten findet."
  3. Rötzer, F.: KI-Programm besser als Menschen im Verständnis natürlicher Sprache (2018) 0.01 
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    Abstract
    Jetzt scheint es allmählich ans Eingemachte zu gehen. Ein von der chinesischen Alibaba-Gruppe entwickelte KI-Programm konnte erstmals Menschen in der Beantwortung von Fragen und dem Verständnis von Text schlagen. Die chinesische Regierung will das Land führend in der Entwicklung von Künstlicher Intelligenz machen und hat dafür eine nationale Strategie aufgestellt. Dazu ernannte das Ministerium für Wissenschaft und Technik die Internetkonzerne Baidu, Alibaba und Tencent sowie iFlyTek zum ersten nationalen Team für die Entwicklung der KI-Technik der nächsten Generation. Baidu ist zuständig für die Entwicklung autonomer Fahrzeuge, Alibaba für die Entwicklung von Clouds für "city brains" (Smart Cities sollen sich an ihre Einwohner und ihre Umgebung anpassen), Tencent für die Enwicklung von Computervision für medizinische Anwendungen und iFlyTec für "Stimmenintelligenz". Die vier Konzerne sollen offene Plattformen herstellen, die auch andere Firmen und Start-ups verwenden können. Überdies wird bei Peking für eine Milliarde US-Dollar ein Technologiepark für die Entwicklung von KI gebaut. Dabei geht es selbstverständlich nicht nur um zivile Anwendungen, sondern auch militärische. Noch gibt es in den USA mehr KI-Firmen, aber China liegt bereits an zweiter Stelle. Das Pentagon ist beunruhigt. Offenbar kommt China rasch vorwärts. Ende 2017 stellte die KI-Firma iFlyTek, die zunächst auf Stimmerkennung und digitale Assistenten spezialisiert war, einen Roboter vor, der den schriftlichen Test der nationalen Medizinprüfung erfolgreich bestanden hatte. Der Roboter war nicht nur mit immensem Wissen aus 53 medizinischen Lehrbüchern, 2 Millionen medizinischen Aufzeichnungen und 400.000 medizinischen Texten und Berichten gefüttert worden, er soll von Medizinexperten klinische Erfahrungen und Falldiagnosen übernommen haben. Eingesetzt werden soll er, in China herrscht vor allem auf dem Land, Ärztemangel, als Helfer, der mit der automatischen Auswertung von Patientendaten eine erste Diagnose erstellt und ansonsten Ärzten mit Vorschlägen zur Seite stehen.
  4. Gachot, D.A.; Lange, E.; Yang, J.: ¬The SYSTRAN NLP browser : an application of machine translation technology in cross-language information retrieval (1998) 0.01 
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    Series
    The Kluwer International series on information retrieval
    Source
    Cross-language information retrieval. Ed.: G. Grefenstette
  5. Smeaton, A.F.: Natural language processing used in information retrieval tasks : an overview of achievements to date (1995) 0.01 
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    Source
    Encyclopedia of library and information science. Vol.55, [=Suppl.18]
  6. Humphreys, K.; Demetriou, G.; Gaizauskas, R.: Bioinformatics applications of information extraction from scientific journal articles (2000) 0.01 
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    Source
    Journal of information science. 26(2000) no.2, S.75-85
  7. Perez-Carballo, J.; Strzalkowski, T.: Natural language information retrieval : progress report (2000) 0.01 
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    Source
    Information processing and management. 36(2000) no.1, S.155-205
  8. Pirkola, A.; Hedlund, T.; Keskustalo, H.; Järvelin, K.: Dictionary-based cross-language information retrieval : problems, methods, and research findings (2001) 0.01 
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    Source
    Information retrieval. 4(2001), S.209-230
  9. Rau, L.F.; Jacobs, P.S.; Zernik, U.: Information extraction and text summarization using linguistic knowledge acquisition (1989) 0.01 
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    Abstract
    Storing and accessing texts in a conceptual format has a number of advantages over traditional document retrieval methods. A conceptual format facilitates natural language access to text information. It can support imprecise and inexact queries, conceptual information summarisation, and, ultimately, document translation. Describes 2 methods which have been implemented in a prototype intelligent information retrieval system calles SCISOR (System for Conceptual Information Summarisation, Organization and Retrieval). Describes the text processing, language acquisition, and summarisation components of SCISOR
    Source
    Information processing and management. 25(1989) no.4, S.419-428
  10. Engerer, V.: Exploring interdisciplinary relationships between linguistics and information retrieval from the 1960s to today (2017) 0.01 
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    Abstract
    This article explores how linguistics has influenced information retrieval (IR) and attempts to explain the impact of linguistics through an analysis of internal developments in information science generally, and IR in particular. It notes that information science/IR has been evolving from a case science into a fully fledged, "disciplined"/disciplinary science. The article establishes correspondences between linguistics and information science/IR using the three established IR paradigms-physical, cognitive, and computational-as a frame of reference. The current relationship between information science/IR and linguistics is elucidated through discussion of some recent information science publications dealing with linguistic topics and a novel technique, "keyword collocation analysis," is introduced. Insights from interdisciplinarity research and case theory are also discussed. It is demonstrated that the three stages of interdisciplinarity, namely multidisciplinarity, interdisciplinarity (in the narrow sense), and transdisciplinarity, can be linked to different phases of the information science/IR-linguistics relationship and connected to different ways of using linguistic theory in information science and IR.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.3, S.660-680
  11. Paolillo, J.C.: Linguistics and the information sciences (2009) 0.01 
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    Abstract
    Linguistics is the scientific study of language which emphasizes language spoken in everyday settings by human beings. It has a long history of interdisciplinarity, both internally and in contribution to other fields, including information science. A linguistic perspective is beneficial in many ways in information science, since it examines the relationship between the forms of meaningful expressions and their social, cognitive, institutional, and communicative context, these being two perspectives on information that are actively studied, to different degrees, in information science. Examples of issues relevant to information science are presented for which the approach taken under a linguistic perspective is illustrated.
    Source
    Encyclopedia of library and information sciences. 3rd ed. Ed.: M.J. Bates
  12. Polity, Y.: Vers une ergonomie linguistique (1994) 0.01 
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    Abstract
    Analyzed a special type of man-mchine interaction, that of searching an information system with natural language. A model for full text processing for information retrieval was proposed that considered the system's users and how they employ information. Describes how INIST (the National Institute for Scientific and Technical Information) is developing computer assisted indexing as an aid to improving relevance when retrieving information from bibliographic data banks
  13. Ciganik, M.: Pred koordinaciou a kooperaciou informacnych systemov (1997) 0.01 
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    Abstract
    The information requirements for library users can only be met if individual information systems are compatible, i.e. based on the use of a single information language. Points out that natural language is the best instrument for integration of information systems. Presents a model of the strucutre of natural language, extended by metaknowledge elements which makes it possible to analyse and represent text without the need for syntax analysis
    Footnote
    Übers. des Titels: Coordination of information systems
  14. Liddy, E.D.: Natural language processing for information retrieval (2009) 0.01 
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    Abstract
    Natural language processing (NLP) is the computerized approach to analyzing text that is based on both a set of theories and a set of technologies. Although NLP is a relatively recent area of research and application, compared with other information technology approaches, there have been sufficient successes to date that suggest that NLP-based information access technologies will continue to be a major area of research and development in information systems now and into the future.
    Source
    Encyclopedia of library and information sciences. 3rd ed. Ed.: M.J. Bates
  15. Metzler, D.P.; Haas, S.W.: ¬The constituent object parser : syntactic structure matching for information retrieval (1989) 0.01 
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    Abstract
    The constituent object parser is designed to improve the precision and recall performance of information retrieval by providing more powerful matching procedures. Describes the dependency tree representations and the relationship between the intended use of the parser and its design.
    Source
    ACM transactions on information systems. 7(1989) no.3, S.292-316
  16. McCune, B.P.; Tong, R.M.; Dean, J.S.: Rubric: a system for rule-based information retrieval (1985) 0.01 
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.440-445.
  17. Byrne, C.C.; McCracken, S.A.: ¬An adaptive thesaurus employing semantic distance, relational inheritance and nominal compound interpretation for linguistic support of information retrieval (1999) 0.01 
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    Source
    Journal of information science. 25(1999) no.2, S.113-131
  18. Ding, Y.; Chowdhury, G.C.; Foo, S.: Incorporating the results of co-word analyses to increase search variety for information retrieval (2000) 0.01 
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    Source
    Journal of information science. 26(2000) no.6, S.429-451
  19. Figuerola, C.G.; Gomez, R.; Lopez de San Roman, E.: Stemming and n-grams in Spanish : an evaluation of their impact in information retrieval (2000) 0.01 
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    Source
    Journal of information science. 26(2000) no.6, S.461-467
  20. Ko, Y.: ¬A new term-weighting scheme for text classification using the odds of positive and negative class probabilities (2015) 0.01 
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    Abstract
    Text classification (TC) is a core technique for text mining and information retrieval. It has been applied to many applications in many different research and industrial areas. Term-weighting schemes assign an appropriate weight to each term to obtain a high TC performance. Although term weighting is one of the important modules for TC and TC has different peculiarities from those in information retrieval, many term-weighting schemes used in information retrieval, such as term frequency-inverse document frequency (tf-idf), have been used in TC in the same manner. The peculiarity of TC that differs most from information retrieval is the existence of class information. This article proposes a new term-weighting scheme that uses class information using positive and negative class distributions. As a result, the proposed scheme, log tf-TRR, consistently performs better than do other schemes using class information as well as traditional schemes such as tf-idf.
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2553-2565

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