Search (56 results, page 1 of 3)

Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.10

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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

Giesselbach, S.; Estler-Ziegler, T.: Dokumente schneller analysieren mit Künstlicher Intelligenz (2021) 0.04
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Abstract

Künstliche Intelligenz (KI) und natürliches Sprachverstehen (natural language understanding/NLU) verändern viele Aspekte unseres Alltags und unserer Arbeitsweise. Besondere Prominenz erlangte NLU durch Sprachassistenten wie Siri, Alexa und Google Now. NLU bietet Firmen und Einrichtungen das Potential, Prozesse effizienter zu gestalten und Mehrwert aus textuellen Inhalten zu schöpfen. So sind NLU-Lösungen in der Lage, komplexe, unstrukturierte Dokumente inhaltlich zu erschließen. Für die semantische Textanalyse hat das NLU-Team des IAIS Sprachmodelle entwickelt, die mit Deep-Learning-Verfahren trainiert werden. Die NLU-Suite analysiert Dokumente, extrahiert Eckdaten und erstellt bei Bedarf sogar eine strukturierte Zusammenfassung. Mit diesen Ergebnissen, aber auch über den Inhalt der Dokumente selbst, lassen sich Dokumente vergleichen oder Texte mit ähnlichen Informationen finden. KI-basierten Sprachmodelle sind der klassischen Verschlagwortung deutlich überlegen. Denn sie finden nicht nur Texte mit vordefinierten Schlagwörtern, sondern suchen intelligent nach Begriffen, die in ähnlichem Zusammenhang auftauchen oder als Synonym gebraucht werden. Der Vortrag liefert eine Einordnung der Begriffe "Künstliche Intelligenz" und "Natural Language Understanding" und zeigt Möglichkeiten, Grenzen, aktuelle Forschungsrichtungen und Methoden auf. Anhand von Praxisbeispielen wird anschließend demonstriert, wie NLU zur automatisierten Belegverarbeitung, zur Katalogisierung von großen Datenbeständen wie Nachrichten und Patenten und zur automatisierten thematischen Gruppierung von Social Media Beiträgen und Publikationen genutzt werden kann.

Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.04

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Source: https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN

Kummer, N.: Indexierungstechniken für das japanische Retrieval (2006) 0.03

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Abstract: Der vorliegende Artikel beschreibt die Herausforderungen, die die japanische Sprache aufgrund der besonderen Struktur ihres Schriftsystems an das Information Retrieval stellt und präsentiert Strategien und Ansätze für die Indexierung japanischer Dokumente. Im Besonderen soll auf die Effektivität aussprachebasierter (yomi-based) Indexierung sowie Fusion verschiedener einzelner Indexierungsansätze eingegangen werden.

Warner, A.J.: Natural language processing (1987) 0.03

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Source: Annual review of information science and technology. 22(1987), S.79-108

McMahon, J.G.; Smith, F.J.: Improved statistical language model performance with automatic generated word hierarchies (1996) 0.02

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Source: Computational linguistics. 22(1996) no.2, S.217-248

Ruge, G.: ¬A spreading activation network for automatic generation of thesaurus relationships (1991) 0.02

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Date: 8.10.2000 11:52:22

Somers, H.: Example-based machine translation : Review article (1999) 0.02

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Date: 31. 7.1996 9:22:19

New tools for human translators (1997) 0.02

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Date: 31. 7.1996 9:22:19

Baayen, R.H.; Lieber, H.: Word frequency distributions and lexical semantics (1997) 0.02

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Date: 28. 2.1999 10:48:22

¬Der Student aus dem Computer (2023) 0.02

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Date: 27. 1.2023 16:22:55

Ruge, G.; Goeser, S.: Information Retrieval ohne Linguistik (1998) 0.02
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Abstract

Natürlicherweise sollte man erwarten, daß linguistische Textanalyseverfahren die Effektivität und Benutzerfreundlichkeit von Information Retrieval Systemen verbessern, da sowohl Dokumente als auch Suchanfragen die interessierenden Inhalte linguistisch enkodieren. Ein Retrievalabgleich auf der Ebene der linguistischen Inhaltsdarstellung müßte demzufolge zu besseren Retrievalsystemen führen als ein Abgleich auf Wort- oder gar Zeichenebene. Tatsächlich aber ist immer noch weitgehend unklar, inwieweit linguistische Textanalyseverfahren Retrievalsysteme verbessern können. Evaluationen von Retrievalsystemen mit linguistischen Komponenten führen nach wie vor zu unterschiedlichen, teils gegenläufigen Ergebnissen, obwohl die dazu erforderliche Computerlinguistik große Fortschritte gemacht hat. Wir gehen der Frage nach, wie es zu diesen kontraintuitiven Ergenissen kommt. Dazu wird der Stand der Kunst im linguistischen IR zusammengefaßt, so daß die Ergebnisse anhand des Vergleich verschiedener Evaluierungen diskutiert werden können.

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) 0.02

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Date: 15. 3.2000 10:22:37

Boleda, G.; Evert, S.: Multiword expressions : a pain in the neck of lexical semantics (2009) 0.02

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Date: 1. 3.2013 14:56:22

Monnerjahn, P.: Vorsprung ohne Technik : Übersetzen: Computer und Qualität (2000) 0.02

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Source: c't. 2000, H.22, S.230-231

Hutchins, J.: From first conception to first demonstration : the nascent years of machine translation, 1947-1954. A chronology (1997) 0.02

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Date: 31. 7.1996 9:22:19

Kuhlmann, U.; Monnerjahn, P.: Sprache auf Knopfdruck : Sieben automatische Übersetzungsprogramme im Test (2000) 0.02

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Source: c't. 2000, H.22, S.220-229

Lezius, W.; Rapp, R.; Wettler, M.: ¬A morphology-system and part-of-speech tagger for German (1996) 0.02

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Date: 22. 3.2015 9:37:18

Wanner, L.: Lexical choice in text generation and machine translation (1996) 0.01

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Date: 31. 7.1996 9:22:19

Riloff, E.: ¬An empirical study of automated dictionary construction for information extraction in three domains (1996) 0.01

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Date: 6. 3.1997 16:22:15

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