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  • × theme_ss:"Computerlinguistik"
  1. Zue, V.: Dein Computer spricht mir dir (1999) 0.00
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    Date
    31.12.1996 19:29:41
  2. Mustafa El Hadi, W.: Evaluating human language technology : general applications to information access and management (2002) 0.00
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    Source
    Knowledge organization. 29(2002) nos.3/4, S.124-134
  3. Müller, P.: Text-Automat mit Tücken (2023) 0.00
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    Source
    Pirmasenser Zeitung. Nr. 29 vom 03.02.2023, S.2
  4. 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) 0.00
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    Date
    15. 3.2000 10:22:37
  5. Boleda, G.; Evert, S.: Multiword expressions : a pain in the neck of lexical semantics (2009) 0.00
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    Date
    1. 3.2013 14:56:22
  6. Monnerjahn, P.: Vorsprung ohne Technik : Übersetzen: Computer und Qualität (2000) 0.00
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    Source
    c't. 2000, H.22, S.230-231
  7. Griffiths, T.L.; Steyvers, M.: ¬A probabilistic approach to semantic representation (2002) 0.00
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    Date
    29. 6.2015 14:55:01
    29. 6.2015 16:09:05
  8. Muresan, S.; Klavans, J.L.: Inducing terminologies from text : a case study for the consumer health domain (2013) 0.00
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    Abstract
    Specialized medical ontologies and terminologies, such as SNOMED CT and the Unified Medical Language System (UMLS), have been successfully leveraged in medical information systems to provide a standard web-accessible medium for interoperability, access, and reuse. However, these clinically oriented terminologies and ontologies cannot provide sufficient support when integrated into consumer-oriented applications, because these applications must "understand" both technical and lay vocabulary. The latter is not part of these specialized terminologies and ontologies. In this article, we propose a two-step approach for building consumer health terminologies from text: 1) automatic extraction of definitions from consumer-oriented articles and web documents, which reflects language in use, rather than relying solely on dictionaries, and 2) learning to map definitions expressed in natural language to terminological knowledge by inducing a syntactic-semantic grammar rather than using hand-written patterns or grammars. We present quantitative and qualitative evaluations of our two-step approach, which show that our framework could be used to induce consumer health terminologies from text.
  9. Perovsek, M.; Kranjca, J.; Erjaveca, T.; Cestnika, B.; Lavraca, N.: TextFlows : a visual programming platform for text mining and natural language processing (2016) 0.00
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    Abstract
    Text mining and natural language processing are fast growing areas of research, with numerous applications in business, science and creative industries. This paper presents TextFlows, a web-based text mining and natural language processing platform supporting workflow construction, sharing and execution. The platform enables visual construction of text mining workflows through a web browser, and the execution of the constructed workflows on a processing cloud. This makes TextFlows an adaptable infrastructure for the construction and sharing of text processing workflows, which can be reused in various applications. The paper presents the implemented text mining and language processing modules, and describes some precomposed workflows. Their features are demonstrated on three use cases: comparison of document classifiers and of different part-of-speech taggers on a text categorization problem, and outlier detection in document corpora.
  10. Olsen, K.A.; Williams, J.G.: Spelling and grammar checking using the Web as a text repository (2004) 0.00
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    Abstract
    Natural languages are both complex and dynamic. They are in part formalized through dictionaries and grammar. Dictionaries attempt to provide definitions and examples of various usages for all the words in a language. Grammar, on the other hand, is the system of rules that defines the structure of a language and is concerned with the correct use and application of the language in speaking or writing. The fact that these two mechanisms lag behind the language as currently used is not a serious problem for those living in a language culture and talking their native language. However, the correct choice of words, expressions, and word relationships is much more difficult when speaking or writing in a foreign language. The basics of the grammar of a language may have been learned in school decades ago, and even then there were always several choices for the correct expression for an idea, fact, opinion, or emotion. Although many different parts of speech and their relationships can make for difficult language decisions, prepositions tend to be problematic for nonnative speakers of English, and, in reality, prepositions are a major problem in most languages. Does a speaker or writer say "in the West Coast" or "on the West Coast," or perhaps "at the West Coast"? In Norwegian, we are "in" a city, but "at" a place. But the distinction between cities and places is vague. To be absolutely correct, one really has to learn the right preposition for every single place. A simplistic way of resolving these language issues is to ask a native speaker. But even native speakers may disagree about the right choice of words. If there is disagreement, then one will have to ask more than one native speaker, treat his/her response as a vote for a particular choice, and perhaps choose the majority choice as the best possible alternative. In real life, such a procedure may be impossible or impractical, but in the electronic world, as we shall see, this is quite easy to achieve. Using the vast text repository of the Web, we may get a significant voting base for even the most detailed and distinct phrases. We shall start by introducing a set of examples to present our idea of using the text repository an the Web to aid in making the best word selection, especially for the use of prepositions. Then we will present a more general discussion of the possibilities and limitations of using the Web as an aid for correct writing.
  11. Czejdo. B.D.; Tucci, R.P.: ¬A dataflow graphical language for database applications (1994) 0.00
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    Date
    20.10.2000 13:29:46
  12. Wenzel, F.: Semantische Eingrenzung im Freitext-Retrieval auf der Basis morphologischer Segmentierungen (1980) 0.00
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    Source
    Nachrichten für Dokumentation. 31(1980) H.1, S.29-35
  13. Whitelock, P.; Kilby, K.: Linguistic and computational techniques in machine translation system design : 2nd ed (1995) 0.00
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    Date
    29. 3.1996 18:28:09
  14. Rau, L.F.: Conceptual information extraction and retrieval from natural language input (198) 0.00
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    Date
    16. 8.1998 13:29:20
  15. Sheremet'eva, S.O.: Teoreticheskie i metodologicheskie problemy inzhenernoi lingvistiki (1998) 0.00
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    Date
    6. 3.1999 13:56:29
  16. Liu, S.; Liu, F.; Yu, C.; Meng, W.: ¬An effective approach to document retrieval via utilizing WordNet and recognizing phrases (2004) 0.00
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    Date
    10.10.2005 10:29:08
  17. Snajder, J.: Distributional semantics of multi-word expressions (2013) 0.00
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    Date
    29. 4.2016 12:04:50
  18. Engerer, V.: Informationswissenschaft und Linguistik. : kurze Geschichte eines fruchtbaren interdisziplinäaren Verhäaltnisses in drei Akten (2012) 0.00
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    Date
    19. 2.2017 13:29:08
  19. Hutchins, J.: From first conception to first demonstration : the nascent years of machine translation, 1947-1954. A chronology (1997) 0.00
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    Date
    31. 7.1996 9:22:19
  20. Kuhlmann, U.; Monnerjahn, P.: Sprache auf Knopfdruck : Sieben automatische Übersetzungsprogramme im Test (2000) 0.00
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    Source
    c't. 2000, H.22, S.220-229

Years

Languages

  • e 111
  • d 43
  • m 2
  • ru 2
  • More… Less…

Types

  • a 128
  • el 20
  • m 16
  • s 8
  • x 4
  • p 2
  • d 1
  • More… Less…

Classifications