Search (151 results, page 1 of 8)

  • × theme_ss:"Computerlinguistik"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.57 
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    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
    Imprint
    Washington, DC : IEEE Computer Society
  2. Huo, W.: Automatic multi-word term extraction and its application to Web-page summarization (2012) 0.43 
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    Content
    A Thesis presented to The University of Guelph In partial fulfilment of requirements for the degree of Master of Science in Computer Science. Vgl. Unter: http://www.inf.ufrgs.br%2F~ceramisch%2Fdownload_files%2Fpublications%2F2009%2Fp01.pdf.
    Date
    10. 1.2013 19:22:47
  3. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.35 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  4. Semantik, Lexikographie und Computeranwendungen : Workshop ... (Bonn) : 1995.01.27-28 (1996) 0.06 
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    Date
    14. 4.2007 10:04:22
    RSWK
    Computer / Anwendung / Computerunterstützte Lexikographie / Aufsatzsammlung
    Subject
    Computer / Anwendung / Computerunterstützte Lexikographie / Aufsatzsammlung
  5. Stieler, W.: Anzeichen von Bewusstsein bei ChatGPT und Co.? (2023) 0.05 
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    Abstract
    Ein interdisziplinäres Forschungsteam hat eine Liste von Eigenschaften aufgestellt, die auf Bewusstsein deuten, und aktuelle KI-Systeme darauf abgeklopft. Ein interdisziplinäres Forscherteam hat ein Paper [https://arxiv.org/abs/2308.08708] veröffentlicht, das eine Liste von 14 "Indikatoren" für Bewusstsein enthält, die aus sechs aktuellen Theorien über das Bewusstsein stammen. Aktuelle KI-Modelle wie GPT-3, Palm-E oder AdA von Deepmind weisen demnach einzelne dieser Indikatoren auf. "Es spricht viel dafür, dass die meisten oder alle Bedingungen für das Bewusstsein, die von derzeitigen Theorien vorgeschlagenen Bedingungen für das Bewusstsein mit den bestehenden Techniken der KI erfüllt werden können", schreiben die Autoren. Zum Team gehörte auch der Deep-Learning-Pionier Yoshua Bengio von der Université de Montréal.
    Source
    https://www.heise.de/hintergrund/Anzeichen-von-Bewusstsein-bei-ChatGPT-und-Co-9295425.html?view=print
  6. Monnerjahn, P.: Vorsprung ohne Technik : Übersetzen: Computer und Qualität (2000) 0.04 
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    Abstract
    Noch immer ist der menschliche Übersetzer dem Computer in sprachlicher Hinsicht überlegen. Zwar ist die Übersetzungssoftware besser geworden, aber die systembedingten Probleme bleiben
    Source
    c't. 2000, H.22, S.230-231
  7. ¬Der Student aus dem Computer (2023) 0.03 
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    Date
    27. 1.2023 16:22:55
  8. Ruge, G.: Sprache und Computer : Wortbedeutung und Termassoziation. Methoden zur automatischen semantischen Klassifikation (1995) 0.03 
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    Content
    Enthält folgende Kapitel: (1) Motivation; (2) Language philosophical foundations; (3) Structural comparison of extensions; (4) Earlier approaches towards term association; (5) Experiments; (6) Spreading-activation networks or memory models; (7) Perspective. Appendices: Heads and modifiers of 'car'. Glossary. Index. Language and computer. Word semantics and term association. Methods towards an automatic semantic classification
    Footnote
    Rez. in: Knowledge organization 22(1995) no.3/4, S.182-184 (M.T. Rolland)
    Series
    Sprache und Computer; Bd.14
  9. Rötzer, F.: Computer ergooglen die Bedeutung von Worten (2005) 0.03 
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    Content
    "Wie könnten Computer Sprache lernen und dabei auch die Bedeutung von Worten sowie die Beziehungen zwischen ihnen verstehen? Dieses Problem der Semantik stellt eine gewaltige, bislang nur ansatzweise bewältigte Aufgabe dar, da Worte und Wortverbindungen oft mehrere oder auch viele Bedeutungen haben, die zudem vom außersprachlichen Kontext abhängen. Die beiden holländischen (Ein künstliches Bewusstsein aus einfachen Aussagen (1)). Paul Vitanyi (2) und Rudi Cilibrasi vom Nationalen Institut für Mathematik und Informatik (3) in Amsterdam schlagen eine elegante Lösung vor: zum Nachschlagen im Internet, der größten Datenbank, die es gibt, wird einfach Google benutzt. Objekte wie eine Maus können mit ihren Namen "Maus" benannt werden, die Bedeutung allgemeiner Begriffe muss aus ihrem Kontext gelernt werden. Ein semantisches Web zur Repräsentation von Wissen besteht aus den möglichen Verbindungen, die Objekte und ihre Namen eingehen können. Natürlich können in der Wirklichkeit neue Namen, aber auch neue Bedeutungen und damit neue Verknüpfungen geschaffen werden. Sprache ist lebendig und flexibel. Um einer Künstlichen Intelligenz alle Wortbedeutungen beizubringen, müsste mit der Hilfe von menschlichen Experten oder auch vielen Mitarbeitern eine riesige Datenbank mit den möglichen semantischen Netzen aufgebaut und dazu noch ständig aktualisiert werden. Das aber müsste gar nicht notwendig sein, denn mit dem Web gibt es nicht nur die größte und weitgehend kostenlos benutzbare semantische Datenbank, sie wird auch ständig von zahllosen Internetnutzern aktualisiert. Zudem gibt es Suchmaschinen wie Google, die Verbindungen zwischen Worten und damit deren Bedeutungskontext in der Praxis in ihrer Wahrscheinlichkeit quantitativ mit der Angabe der Webseiten, auf denen sie gefunden wurden, messen.
    Mit einem bereits zuvor von Paul Vitanyi und anderen entwickeltem Verfahren, das den Zusammenhang von Objekten misst (normalized information distance - NID ), kann die Nähe zwischen bestimmten Objekten (Bilder, Worte, Muster, Intervalle, Genome, Programme etc.) anhand aller Eigenschaften analysiert und aufgrund der dominanten gemeinsamen Eigenschaft bestimmt werden. Ähnlich können auch die allgemein verwendeten, nicht unbedingt "wahren" Bedeutungen von Namen mit der Google-Suche erschlossen werden. 'At this moment one database stands out as the pinnacle of computer-accessible human knowledge and the most inclusive summary of statistical information: the Google search engine. There can be no doubt that Google has already enabled science to accelerate tremendously and revolutionized the research process. It has dominated the attention of internet users for years, and has recently attracted substantial attention of many Wall Street investors, even reshaping their ideas of company financing.' (Paul Vitanyi und Rudi Cilibrasi) Gibt man ein Wort ein wie beispielsweise "Pferd", erhält man bei Google 4.310.000 indexierte Seiten. Für "Reiter" sind es 3.400.000 Seiten. Kombiniert man beide Begriffe, werden noch 315.000 Seiten erfasst. Für das gemeinsame Auftreten beispielsweise von "Pferd" und "Bart" werden zwar noch immer erstaunliche 67.100 Seiten aufgeführt, aber man sieht schon, dass "Pferd" und "Reiter" enger zusammen hängen. Daraus ergibt sich eine bestimmte Wahrscheinlichkeit für das gemeinsame Auftreten von Begriffen. Aus dieser Häufigkeit, die sich im Vergleich mit der maximalen Menge (5.000.000.000) an indexierten Seiten ergibt, haben die beiden Wissenschaftler eine statistische Größe entwickelt, die sie "normalised Google distance" (NGD) nennen und die normalerweise zwischen 0 und 1 liegt. Je geringer NGD ist, desto enger hängen zwei Begriffe zusammen. "Das ist eine automatische Bedeutungsgenerierung", sagt Vitanyi gegenüber dern New Scientist (4). "Das könnte gut eine Möglichkeit darstellen, einen Computer Dinge verstehen und halbintelligent handeln zu lassen." Werden solche Suchen immer wieder durchgeführt, lässt sich eine Karte für die Verbindungen von Worten erstellen. Und aus dieser Karte wiederum kann ein Computer, so die Hoffnung, auch die Bedeutung der einzelnen Worte in unterschiedlichen natürlichen Sprachen und Kontexten erfassen. So habe man über einige Suchen realisiert, dass ein Computer zwischen Farben und Zahlen unterscheiden, holländische Maler aus dem 17. Jahrhundert und Notfälle sowie Fast-Notfälle auseinander halten oder elektrische oder religiöse Begriffe verstehen könne. Überdies habe eine einfache automatische Übersetzung Englisch-Spanisch bewerkstelligt werden können. Auf diese Weise ließe sich auch, so hoffen die Wissenschaftler, die Bedeutung von Worten erlernen, könne man Spracherkennung verbessern oder ein semantisches Web erstellen und natürlich endlich eine bessere automatische Übersetzung von einer Sprache in die andere realisieren.
  10. Informationslinguistische Texterschließung (1986) 0.02 
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    RSWK
    Information Retrieval / Aufsatzsammlung (DNB)
    Automatische Sprachanalyse / Morphologie / Aufsatzsammlung (SBB / GBV)
    Automatische Sprachanalyse / Morphologie <Linguistik> / Aufsatzsammlung (DNB)
    Linguistische Datenverarbeitung / Linguistik / Aufsatzsammlung (SWB)
    Linguistik / Information Retrieval / Aufsatzsammlung (SWB / BVB)
    Linguistische Datenverarbeitung / Textanalyse / Aufsatzsammlung (BVB)
    Subject
    Information Retrieval / Aufsatzsammlung (DNB)
    Automatische Sprachanalyse / Morphologie / Aufsatzsammlung (SBB / GBV)
    Automatische Sprachanalyse / Morphologie <Linguistik> / Aufsatzsammlung (DNB)
    Linguistische Datenverarbeitung / Linguistik / Aufsatzsammlung (SWB)
    Linguistik / Information Retrieval / Aufsatzsammlung (SWB / BVB)
    Linguistische Datenverarbeitung / Textanalyse / Aufsatzsammlung (BVB)
  11. Information und Sprache : Beiträge zu Informationswissenschaft, Computerlinguistik, Bibliothekswesen und verwandten Fächern. Festschrift für Harald H. Zimmermann. Herausgegeben von Ilse Harms, Heinz-Dirk Luckhardt und Hans W. Giessen (2006) 0.02 
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    Content
    Inhalt: Information und Sprache und mehr - eine Einleitung - Information und Kommunikation Wolf Rauch: Auch Information ist eine Tochter der Zeit Winfried Lenders: Information und kulturelles Gedächtnis Rainer Hammwöhner: Anmerkungen zur Grundlegung der Informationsethik Hans W. Giessen: Ehrwürdig stille Informationen Gernot Wersig: Vereinheitlichte Medientheorie und ihre Sicht auf das Internet Johann Haller, Anja Rütten: Informationswissenschaft und Translationswissenschaft: Spielarten oder Schwestern? Rainer Kuhlen: In Richtung Summarizing für Diskurse in K3 Werner Schweibenz: Sprache, Information und Bedeutung im Museum. Narrative Vermittlung durch Storytelling - Sprache und Computer, insbesondere Information Retrieval und Automatische Indexierung Manfred Thiel: Bedingt wahrscheinliche Syntaxbäume Jürgen Krause: Shell Model, Semantic Web and Web Information Retrieval Elisabeth Niggemann: Wer suchet, der findet? Verbesserung der inhaltlichen Suchmöglichkeiten im Informationssystem Der Deutschen Bibliothek Christa Womser-Hacker: Zur Rolle von Eigennamen im Cross-Language Information Retrieval Klaus-Dirk Schmitz: Wörterbuch, Thesaurus, Terminologie, Ontologie. Was tragen Terminologiewissenschaft und Informationswissenschaft zur Wissensordnung bei?
    Footnote
    Rez. in Mitt. VÖB 59(2006) Nr.3, S.75-78 (O. Oberhauser): "Beim vorliegenden Buch handelt es sich um die Festschrift zum 65. Geburtstag des mit Ende des Sommersemesters 2006 in den Ruhestand getretenen Universitätsprofessors für Informationswissenschaft, Harald H. Zimmermann, jenes 1941 in Völklingen geborenen Computerlinguisten, der die Informationswissenschaft als akademische Disziplin in Deutschland mitbegründet und seit 1980 an der Universität des Saarlandes vertreten hat. Die insgesamt 26 Beiträge des von Professor Zimmermanns Mitarbeitern betreuten, optisch gediegen anmutenden Saur-Bandes gliedern sich - so das Inhaltsverzeichnis - in vier Themenschwerpunkte: - Information und Kommunikation - Sprache und Computer, insbesondere Information Retrieval und Automatische Indexierung - Analysen und Entwicklungen - Persönliches Die Aufsätze selbst variieren, wie bei Festschriften üblich bzw. unvermeidbar, hinsichtlich Länge, Stil, thematischem Detail und Anspruchsniveau. Neben wissenschaftlichen Beiträgen findet man hier auch Reminiszenzen und Literarisches. Die nachfolgende Auswahl zeigt, was mich selbst an diesem Buch interessiert hat:
    RSWK
    Informations- und Dokumentationswissenschaft / Aufsatzsammlung
    Information Retrieval / Aufsatzsammlung
    Automatische Indexierung / Aufsatzsammlung
    Linguistische Datenverarbeitung / Aufsatzsammlung
    Subject
    Informations- und Dokumentationswissenschaft / Aufsatzsammlung
    Information Retrieval / Aufsatzsammlung
    Automatische Indexierung / Aufsatzsammlung
    Linguistische Datenverarbeitung / Aufsatzsammlung
  12. Rieger, F.: Lügende Computer (2023) 0.02 
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    Date
    16. 3.2023 19:22:55
  13. Translating and the computer : 18th International Conference on Translating on the Computer (1996) 0.02 
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  14. Goshawke, W.; Kelly, D.K.; Wigg, J.D.: Computer translation of natural language (1987) 0.02 
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    PRECIS
    Languages / Translation / Applications of computer systems
    Subject
    Languages / Translation / Applications of computer systems
  15. Doszkocs, T.E.; Zamora, A.: Dictionary services and spelling aids for Web searching (2004) 0.02 
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    Abstract
    The Specialized Information Services Division (SIS) of the National Library of Medicine (NLM) provides Web access to more than a dozen scientific databases on toxicology and the environment on TOXNET . Search queries on TOXNET often include misspelled or variant English words, medical and scientific jargon and chemical names. Following the example of search engines like Google and ClinicalTrials.gov, we set out to develop a spelling "suggestion" system for increased recall and precision in TOXNET searching. This paper describes development of dictionary technology that can be used in a variety of applications such as orthographic verification, writing aid, natural language processing, and information storage and retrieval. The design of the technology allows building complex applications using the components developed in the earlier phases of the work in a modular fashion without extensive rewriting of computer code. Since many of the potential applications envisioned for this work have on-line or web-based interfaces, the dictionaries and other computer components must have fast response, and must be adaptable to open-ended database vocabularies, including chemical nomenclature. The dictionary vocabulary for this work was derived from SIS and other databases and specialized resources, such as NLM's Unified Medical Language Systems (UMLS) . The resulting technology, A-Z Dictionary (AZdict), has three major constituents: 1) the vocabulary list, 2) the word attributes that define part of speech and morphological relationships between words in the list, and 3) a set of programs that implements the retrieval of words and their attributes, and determines similarity between words (ChemSpell). These three components can be used in various applications such as spelling verification, spelling aid, part-of-speech tagging, paraphrasing, and many other natural language processing functions.
    Date
    14. 8.2004 17:22:56
    Source
    Online. 28(2004) no.3, S.22-29
  16. Kay, M.: ¬The proper place of men and machines in language translation (1997) 0.02 
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    Abstract
    Machine translation stands no chance of filling actual needs for translation because, although there has been progress in relevant areas of computer science, advance in linguistics have not touched the core problems. Cooperative man-machine systems need to be developed, Proposes a translator's amanuensis, incorporating into a word processor some simple facilities peculiar to translation. Gradual enhancements of such a system could lead to the original goal of machine translation
    Date
    31. 7.1996 9:22:19
  17. Possibilities in computer content analysis of text (1997) 0.01 
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    Footnote
    A symposium based on presentations made at a panel of the 7th annual Conference of the Social Science Computing Association entitled Possibilities in Computer Content Analysis of Text, Minneapolis, Minnesota, USA, 1996
    Source
    Social science computer review. 15(1997) no.4, S.349-409
  18. Yang, C.C.; Luk, J.: Automatic generation of English/Chinese thesaurus based on a parallel corpus in laws (2003) 0.01 
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    Abstract
    The information available in languages other than English in the World Wide Web is increasing significantly. According to a report from Computer Economics in 1999, 54% of Internet users are English speakers ("English Will Dominate Web for Only Three More Years," Computer Economics, July 9, 1999, http://www.computereconomics. com/new4/pr/pr990610.html). However, it is predicted that there will be only 60% increase in Internet users among English speakers verses a 150% growth among nonEnglish speakers for the next five years. By 2005, 57% of Internet users will be non-English speakers. A report by CNN.com in 2000 showed that the number of Internet users in China had been increased from 8.9 million to 16.9 million from January to June in 2000 ("Report: China Internet users double to 17 million," CNN.com, July, 2000, http://cnn.org/2000/TECH/computing/07/27/ china.internet.reut/index.html). According to Nielsen/ NetRatings, there was a dramatic leap from 22.5 millions to 56.6 millions Internet users from 2001 to 2002. China had become the second largest global at-home Internet population in 2002 (US's Internet population was 166 millions) (Robyn Greenspan, "China Pulls Ahead of Japan," Internet.com, April 22, 2002, http://cyberatias.internet.com/big-picture/geographics/article/0,,5911_1013841,00. html). All of the evidences reveal the importance of crosslingual research to satisfy the needs in the near future. Digital library research has been focusing in structural and semantic interoperability in the past. Searching and retrieving objects across variations in protocols, formats and disciplines are widely explored (Schatz, B., & Chen, H. (1999). Digital libraries: technological advances and social impacts. IEEE Computer, Special Issue an Digital Libraries, February, 32(2), 45-50.; Chen, H., Yen, J., & Yang, C.C. (1999). International activities: development of Asian digital libraries. IEEE Computer, Special Issue an Digital Libraries, 32(2), 48-49.). However, research in crossing language boundaries, especially across European languages and Oriental languages, is still in the initial stage. In this proposal, we put our focus an cross-lingual semantic interoperability by developing automatic generation of a cross-lingual thesaurus based an English/Chinese parallel corpus. When the searchers encounter retrieval problems, Professional librarians usually consult the thesaurus to identify other relevant vocabularies. In the problem of searching across language boundaries, a cross-lingual thesaurus, which is generated by co-occurrence analysis and Hopfield network, can be used to generate additional semantically relevant terms that cannot be obtained from dictionary. In particular, the automatically generated cross-lingual thesaurus is able to capture the unknown words that do not exist in a dictionary, such as names of persons, organizations, and events. Due to Hong Kong's unique history background, both English and Chinese are used as official languages in all legal documents. Therefore, English/Chinese cross-lingual information retrieval is critical for applications in courts and the government. In this paper, we develop an automatic thesaurus by the Hopfield network based an a parallel corpus collected from the Web site of the Department of Justice of the Hong Kong Special Administrative Region (HKSAR) Government. Experiments are conducted to measure the precision and recall of the automatic generated English/Chinese thesaurus. The result Shows that such thesaurus is a promising tool to retrieve relevant terms, especially in the language that is not the same as the input term. The direct translation of the input term can also be retrieved in most of the cases.
  19. Alvaro, S.J.: Understanding editorial text : a computer model of argument comprehension (19??) 0.01 
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    Series
    Kluwer international series in engineering and computer science; natural language processing and machine translation
  20. Samad, T.: ¬A natural language interface for computer aided design (19??) 0.01 
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    Series
    Kluwer international series in engineering and computer science; natural language processing and machine translation

Authors

Years

Languages

  • e 102
  • d 47
  • m 2
  • f 1
  • ru 1
  • More… Less…

Types

  • a 93
  • m 41
  • s 18
  • el 12
  • x 3
  • d 2
  • p 2
  • pat 1
  • More… Less…

Themes

Subjects

Classifications