Search (106 results, page 1 of 6)

  • × theme_ss:"Computerlinguistik"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.18 
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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
  2. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.11 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  3. Weingarten, R.: ¬Die Verkabelung der Sprache : Grenzen der Technisierung von Kommunikation (1989) 0.10 
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    Classification
    Spr B 191 / Kommunikation
    SBB
    Spr B 191 / Kommunikation
  4. Huo, W.: Automatic multi-word term extraction and its application to Web-page summarization (2012) 0.09 
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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
  5. Sprachtechnologie : ein Überblick (2012) 0.05 
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    Abstract
    Seit mehr als einem halben Jahrhundert existieren ernsthafte und ernst zu nehmende Versuche, menschliche Sprache maschinell zu verarbeiten. Maschinelle Übersetzung oder "natürliche" Dialoge mit Computern gehören zu den ersten Ideen, die den Bereich der späteren Computerlinguistik oder Sprachtechnologie abgesteckt und deren Vorhaben geleitet haben. Heute ist dieser auch maschinelle Sprachverarbeitung (natural language processing, NLP) genannte Bereich stark ausdiversifiziert: Durch die rapide Entwicklung der Informatik ist vieles vorher Unvorstellbare Realität (z. B. automatische Telefonauskunft), einiges früher Unmögliche immerhin möglich geworden (z. B. Handhelds mit Sprachein- und -ausgabe als digitale persönliche (Informations-)Assistenten). Es gibt verschiedene Anwendungen der Computerlinguistik, von denen einige den Sprung in die kommerzielle Nutzung geschafft haben (z. B. Diktiersysteme, Textklassifikation, maschinelle Übersetzung). Immer noch wird an natürlichsprachlichen Systemen (natural language systems, NLS) verschiedenster Funktionalität (z. B. zur Beantwortung beliebiger Fragen oder zur Generierung komplexer Texte) intensiv geforscht, auch wenn die hoch gesteckten Ziele von einst längst nicht erreicht sind (und deshalb entsprechend "heruntergefahren" wurden). Wo die maschinelle Sprachverarbeitung heute steht, ist allerdings angesichts der vielfältigen Aktivitäten in der Computerlinguistik und Sprachtechnologie weder offensichtlich noch leicht in Erfahrung zu bringen (für Studierende des Fachs und erst recht für Laien). Ein Ziel dieses Buches ist, es, die aktuelle Literaturlage in dieser Hinsicht zu verbessern, indem spezifisch systembezogene Aspekte der Computerlinguistik als Überblick über die Sprachtechnologie zusammengetragen werden.
  6. Winterschladen, S.; Gurevych, I.: ¬Die perfekte Suchmaschine : Forschungsgruppe entwickelt ein System, das artverwandte Begriffe finden soll (2006) 0.04 
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    Content
    "KÖLNER STADT-ANZEIGER: Frau Gurevych, Sie entwickeln eine Suchmaschine der nächsten Generation? Wie kann man sich diese vorstellen? IRYNA GUREVYCH Jeder kennt die herkömmlichen Suchmaschinen wie Google, Yahoo oder Altavista. Diese sind aber nicht perfekt, weil sie nur nach dem Prinzip der Zeichenerkennung funktionieren. Das steigende Informationsbedürfnis können herkömmliche Suchmaschinen nicht befriedigen. KStA: Wieso nicht? GUREVYCH Nehmen wir mal ein konkretes Beispiel: Sie suchen bei Google nach einem Rezept für einen Kuchen, der aber kein Obst enthalten soll. Keine Suchmaschine der Welt kann bisher sinnvoll solche oder ähnliche Anfragen ausführen. Meistens kommen Tausende von Ergebnissen, in denen der Nutzer die relevanten Informationen wie eine Nadel im Heuhaufen suchen muss. KStA: Und Sie können dieses Problem lösen? GUREVYCH Wir entwickeln eine Suchmaschine, die sich nicht nur auf das System der Zeichenerkennung verlässt, sondern auch linguistische Merkmale nutzt. Unsere Suchmaschine soll also auch artverwandte Begriffe zeigen. KStA: Wie weit sind Sie mit Ihrer Forschung? GUREVYCH Das Projekt ist auf zwei Jahre angelegt. Wir haben vor einem halben Jahr begonnen, haben also noch einen großen Teil vor uns. Trotzdem sind die ersten Zwischenergebnisse schon sehr beachtlich. KStA: Und wann geht die Suchmaschine ins Internet? GUREVYCH Da es sich um ein Projekt der Deutschen Forschungsgemeinschaft handelt, wird die Suchmaschine vorerst nicht veröffentlicht. Wir sehen es als unsere Aufgabe an, Verbesserungsmöglichkeiten durch schlaue Such-Algorithmen mit unseren Forschungsarbeiten nachzuweisen und Fehler der bekannten Suchmaschinen zu beseitigen. Und da sind wir auf einem guten Weg. KStA: Arbeiten Sie auch an einem ganz speziellen Projekt? GUREVYCH Ja, ihre erste Bewährungsprobe muss die neue Technologie auf einem auf den ersten Blick ungewöhnlichen Feld bestehen: Unsere Forschungsgruppe an der Technischen Universität Darmstadt entwickelt derzeit ein neuartiges System zur Unterstützung Jugendlicher bei der Berufsauswahl. Dazu stellt uns die Bundesagentur für Arbeit die Beschreibungen von 5800 Berufen in Deutschland zur Verfügung. KStA: Und was sollen Sie dann mit diesen konkreten Informationen machen? GUREVYCH Jugendliche sollen unsere Suchmaschine mit einem Aufsatz über ihre beruflichen Vorlieben flittern. Das System soll dann eine Suchabfrage starten und mögliche Berufe anhand des Interesses des Jugendlichen heraussuchen. Die persönliche Beratung durch die Bundesagentur für Arbeit kann dadurch auf alternative Angebote ausgeweitet werden. Ein erster Prototyp soll Ende des Jahres bereitstehen. KStA: Es geht also zunächst einmal nicht darum, einen Jobfür den Jugendlichen zu finden, sondern den perfekten Beruf für ihn zu ermitteln? GUREVYCH Ja, anhand der Beschreibung des Jugendlichen startet die Suchmaschine eine semantische Abfrage und sucht den passenden Beruf heraus. KStA: Gab es schon weitere Anfragen seitens der Industrie? GUREVYCH Nein, wir haben bisher noch keine Werbung betrieben. Meine Erfahrung zeigt, dass angesehene Kongresse die beste Plattform sind, um die Ergebnisse zu präsentieren und auf sich aufmerksam zu machen. Einige erste Veröffentlichungen sind bereits unterwegs und werden 2006 noch erscheinen. KStA: Wie sieht denn Ihrer Meinung nach die Suchmaschine der Zukunft aus? GUREVYCH Suchmaschinen werden immer spezieller. Das heißt, dass es etwa in der Medizin, bei den Krankenkassen oder im Sport eigene Suchmaschinen geben wird. Außerdem wird die Tendenz verstärkt zu linguistischen Suchmaschinen gehen, die nach artverwandten Begriffen fahnden. Die perfekte Suchmaschine wird wohl eine Kombination aus statistischem und linguistisch-semantischem Suchverhalten sein. Algorithmen, die wir am Fachgebiet Telekooperation an der TU Darmstadt entwickeln, werden für den nächsten qualitativen Sprung bei der Entwicklung der Suchmaschinen von größter Bedeutung sein."
    Source
    Kölner Stadtanzeiger. Nr.191 vom 18.8.2006, Magazin S.7
  7. Semantik, Lexikographie und Computeranwendungen : Workshop ... (Bonn) : 1995.01.27-28 (1996) 0.02 
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    Classification
    Spr B 68 / Computerlinguistik
    Date
    14. 4.2007 10:04:22
    SBB
    Spr B 68 / Computerlinguistik
  8. Endres-Niggemeyer, B.: Sprachverarbeitung im Informationsbereich (1989) 0.02 
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  9. Natürlichsprachlicher Entwurf von Informationssystemen (1996) 0.02 
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    Editor
    Ortner, E., B. Schienmann u. H. Thoma
  10. Luo, L.; Ju, J.; Li, Y.-F.; Haffari, G.; Xiong, B.; Pan, S.: ChatRule: mining logical rules with large language models for knowledge graph reasoning (2023) 0.02 
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    Date
    23.11.2023 19:07:22
  11. Caseiro, D.: Automatic language identification bibliography : Last Update: 20 September 1999 (1999) 0.02 
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  12. Campe, P.: Case, semantic roles, and grammatical relations : a comprehensive bibliography (1994) 0.01 
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  13. Vichot, F.; Wolinksi, F.; Tomeh, J.; Guennou, S.; Dillet, B.; Aydjian, S.: High precision hypertext navigation based on NLP automation extractions (1997) 0.01 
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  14. Jones, D.: Analogical natural language processing (1996) 0.01 
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    Spr B 68 / Computerlinguistik
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  15. 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.
  16. Sabourin, C.F. (Bearb.): Computational linguistics in information science : bibliography (1994) 0.01 
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  17. Dreehsen, B.: ¬Der PC als Dolmetscher (1998) 0.01 
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  18. Rettinger, A.; Schumilin, A.; Thoma, S.; Ell, B.: Learning a cross-lingual semantic representation of relations expressed in text (2015) 0.01 
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  19. Hofstadter, D.: Artificial neural networks today are not conscious (2022) 0.01 
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    Content
    Vgl. auch: Agüera y Arcas, B.: Artificial neural networks are making strides towards consciousness..
  20. Agüera y Arcas, B.: Artificial neural networks are making strides towards consciousness (2022) 0.01 
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