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  • × theme_ss:"Computerlinguistik"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.31
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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) 0.22
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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) 0.17
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  4. Roberts, C.W.; Popping, R.: Computer-supported content analysis : some recent developments (1993) 0.04
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    Abstract
    Presents an overview of some recent developments in the clause-based content analysis of linguistic data. Introduces network analysis of evaluative texts, for the analysis of cognitive maps, and linguistic content analysis. Focuses on the types of substantive inferences afforded by the three approaches
    Source
    Social science computer review. 11(1993) no.3, S.283-291
  5. Ruge, G.: ¬A spreading activation network for automatic generation of thesaurus relationships (1991) 0.04
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    Date
    8.10.2000 11:52:22
  6. Yang, C.C.; Luk, J.: Automatic generation of English/Chinese thesaurus based on a parallel corpus in laws (2003) 0.04
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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.
  7. Monnerjahn, P.: Vorsprung ohne Technik : Übersetzen: Computer und Qualität (2000) 0.03
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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
  8. ¬Der Student aus dem Computer (2023) 0.03
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    Date
    27. 1.2023 16:22:55
  9. Doszkocs, T.E.; Zamora, A.: Dictionary services and spelling aids for Web searching (2004) 0.03
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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
  10. 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
  11. Fóris, A.: Network theory and terminology (2013) 0.03
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    Abstract
    The paper aims to present the relations of network theory and terminology. The model of scale-free networks, which has been recently developed and widely applied since, can be effectively used in terminology research as well. Operation based on the principle of networks is a universal characteristic of complex systems. Networks are governed by general laws. The model of scale-free networks can be viewed as a statistical-probability model, and it can be described with mathematical tools. Its main feature is that "everything is connected to everything else," that is, every node is reachable (in a few steps) starting from any other node; this phenomena is called "the small world phenomenon." The existence of a linguistic network and the general laws of the operation of networks enable us to place issues of language use in the complex system of relations that reveal the deeper connection s between phenomena with the help of networks embedded in each other. The realization of the metaphor that language also has a network structure is the basis of the classification methods of the terminological system, and likewise of the ways of creating terminology databases, which serve the purpose of providing easy and versatile accessibility to specialised knowledge.
    Date
    2. 9.2014 21:22:48
  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. Fellbaum, C.: ¬A semantic network of English : the mother of all WordNets (1998) 0.02
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  15. 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
  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. Semantik, Lexikographie und Computeranwendungen : Workshop ... (Bonn) : 1995.01.27-28 (1996) 0.01
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    Date
    14. 4.2007 10:04:22
    RSWK
    Computer / Anwendung / Computerunterstützte Lexikographie / Aufsatzsammlung
    Subject
    Computer / Anwendung / Computerunterstützte Lexikographie / Aufsatzsammlung
  18. Morris, V.: Automated language identification of bibliographic resources (2020) 0.01
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    Abstract
    This article describes experiments in the use of machine learning techniques at the British Library to assign language codes to catalog records, in order to provide information about the language of content of the resources described. In the first phase of the project, language codes were assigned to 1.15 million records with 99.7% confidence. The automated language identification tools developed will be used to contribute to future enhancement of over 4 million legacy records.
    Date
    2. 3.2020 19:04:22
  19. Vechtomova, O.; Karamuftuoglum, M.; Robertson, S.E.: On document relevance and lexical cohesion between query terms (2006) 0.01
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    Abstract
    Lexical cohesion is a property of text, achieved through lexical-semantic relations between words in text. Most information retrieval systems make use of lexical relations in text only to a limited extent. In this paper we empirically investigate whether the degree of lexical cohesion between the contexts of query terms' occurrences in a document is related to its relevance to the query. Lexical cohesion between distinct query terms in a document is estimated on the basis of the lexical-semantic relations (repetition, synonymy, hyponymy and sibling) that exist between there collocates - words that co-occur with them in the same windows of text. Experiments suggest significant differences between the lexical cohesion in relevant and non-relevant document sets exist. A document ranking method based on lexical cohesion shows some performance improvements.
  20. Radev, D.R.; Joseph, M.T.; Gibson, B.; Muthukrishnan, P.: ¬A bibliometric and network analysis of the field of computational linguistics (2016) 0.01
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    Abstract
    The ACL Anthology is a large collection of research papers in computational linguistics. Citation data were obtained using text extraction from a collection of PDF files with significant manual postprocessing performed to clean up the results. Manual annotation of the references was then performed to complete the citation network. We analyzed the networks of paper citations, author citations, and author collaborations in an attempt to identify the most central papers and authors. The analysis includes general network statistics, PageRank, metrics across publication years and venues, the impact factor and h-index, as well as other measures.

Authors

Languages

  • e 150
  • d 47
  • m 3
  • f 2
  • ru 1
  • More… Less…

Types

  • a 144
  • m 40
  • s 16
  • el 14
  • x 4
  • d 2
  • p 2
  • pat 1
  • More… Less…

Subjects

Classifications