Search (102 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) 0.07 
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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. AI-Sughaiyer, I.A.; AI-Kharashi, I.A.: Arabic morphological analysis techniques : a comprehensive survey (2004) 0.04 
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    Abstract
    After several decades of heavy research activity an English stemmers, Arabic morphological analysis techniques have become a popular area of research. The Arabic language is one of the Semitic languages; it exhibits a very systematic but complex morphological structure based an root-pattern schemes. As a consequence, survey of such techniques proves to be more necessary. The aim of this paper is to summarize and organize the information available in the literature in an attempt to motivate researchers to look into these techniques and try to develop more advanced ones. This paper introduces, classifies, and surveys Arabic morphological analysis techniques. Furthermore, conclusions, open areas, and future directions are provided at the end.
  3. Kuo, J.-S.; Li, H.; Yang, Y.-K.: Active learning for constructing transliteration lexicons from the Web (2008) 0.04 
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    Abstract
    This article presents an adaptive learning framework for Phonetic Similarity Modeling (PSM) that supports the automatic construction of transliteration lexicons. The learning algorithm starts with minimum prior knowledge about machine transliteration and acquires knowledge iteratively from the Web. We study the unsupervised learning and the active learning strategies that minimize human supervision in terms of data labeling. The learning process refines the PSM and constructs a transliteration lexicon at the same time. We evaluate the proposed PSM and its learning algorithm through a series of systematic experiments, which show that the proposed framework is reliably effective on two independent databases.
  4. Schwarz, C.: THESYS: Thesaurus Syntax System : a fully automatic thesaurus building aid (1988) 0.03 
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    Abstract
    THESYS is based on the natural language processing of free-text databases. It yields statistically evaluated correlations between words of the database. These correlations correspond to traditional thesaurus relations. The person who has to build a thesaurus is thus assisted by the proposals made by THESYS. THESYS is being tested on commercial databases under real world conditions. It is part of a text processing project at Siemens, called TINA (Text-Inhalts-Analyse). Software from TINA is actually being applied and evaluated by the US Department of Commerce for patent search and indexing (REALIST: REtrieval Aids by Linguistics and STatistics)
    Date
    6. 1.1999 10:22:07
  5. Zhai, X.: ChatGPT user experience: : implications for education (2022) 0.03 
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    Abstract
    ChatGPT, a general-purpose conversation chatbot released on November 30, 2022, by OpenAI, is expected to impact every aspect of society. However, the potential impacts of this NLP tool on education remain unknown. Such impact can be enormous as the capacity of ChatGPT may drive changes to educational learning goals, learning activities, and assessment and evaluation practices. This study was conducted by piloting ChatGPT to write an academic paper, titled Artificial Intelligence for Education (see Appendix A). The piloting result suggests that ChatGPT is able to help researchers write a paper that is coherent, (partially) accurate, informative, and systematic. The writing is extremely efficient (2-3 hours) and involves very limited professional knowledge from the author. Drawing upon the user experience, I reflect on the potential impacts of ChatGPT, as well as similar AI tools, on education. The paper concludes by suggesting adjusting learning goals-students should be able to use AI tools to conduct subject-domain tasks and education should focus on improving students' creativity and critical thinking rather than general skills. To accomplish the learning goals, researchers should design AI-involved learning tasks to engage students in solving real-world problems. ChatGPT also raises concerns that students may outsource assessment tasks. This paper concludes that new formats of assessments are needed to focus on creativity and critical thinking that AI cannot substitute.
  6. Rahmstorf, G.: Concept structures for large vocabularies (1998) 0.03 
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    Abstract
    A technology is described which supports the acquisition, visualisation and manipulation of large vocabularies with associated structures. It is used for dictionary production, terminology data bases, thesauri, library classification systems etc. Essential features of the technology are a lexicographic user interface, variable word description, unlimited list of word readings, a concept language, automatic transformations of formulas into graphic structures, structure manipulation operations and retransformation into formulas. The concept language includes notations for undefined concepts. The structure of defined concepts can be constructed interactively. The technology supports the generation of large vocabularies with structures representing word senses. Concept structures and ordering systems for indexing and retrieval can be constructed separately and connected by associating relations.
    Date
    30.12.2001 19:01:22
  7. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.03 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  8. Lustig, G.: ¬Das Projekt WAI : Wörterbuchentwicklung für automatisches Indexing (1982) 0.02 
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  9. Warner, A.J.: ¬The role of linguistic analysis in full-text retrieval (1994) 0.02 
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    Source
    Challenges in indexing electronic text and images. Ed.: R. Fidel et al
  10. Chou, C.; Chu, T.: ¬An analysis of BERT (NLP) for assisted subject indexing for Project Gutenberg (2022) 0.02 
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    Abstract
    In light of AI (Artificial Intelligence) and NLP (Natural language processing) technologies, this article examines the feasibility of using AI/NLP models to enhance the subject indexing of digital resources. While BERT (Bidirectional Encoder Representations from Transformers) models are widely used in scholarly communities, the authors assess whether BERT models can be used in machine-assisted indexing in the Project Gutenberg collection, through suggesting Library of Congress subject headings filtered by certain Library of Congress Classification subclass labels. The findings of this study are informative for further research on BERT models to assist with automatic subject indexing for digital library collections.
  11. Garfield, E.: ¬The relationship between mechanical indexing, structural linguistics and information retrieval (1992) 0.02 
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    Abstract
    It is possible to locate over 60% of indexing terms used in the Current List of Medical Literature by analysing the titles of the articles. Citation indexes contain 'noise' and lack many pertinent citations. Mechanical indexing or analysis of text must begin with some linguistic technique. Discusses Harris' methods of structural linguistics, discourse analysis and transformational analysis. Provides 3 examples with references, abstracts and index entries
  12. Warner, A.J.: Natural language processing (1987) 0.02 
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    Source
    Annual review of information science and technology. 22(1987), S.79-108
  13. Smeaton, A.F.: Progress in the application of natural language processing to information retrieval tasks (1992) 0.02 
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    Abstract
    Account of recent developments in automatic and semi-automatic text indexing as well as in the generation of thesauri, text retrieval, abstracting and summarization
  14. Hagn-Meincke, L.L.: Sprogspil pa tvaers : sprogfilosofiske teoriers betydning for indeksering og emnesogning (1999) 0.02 
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    Footnote
    Übers. d. Titels: Language-game interferences: the importance of linguistic theories for indexing and subject searching
  15. Godby, C.J.; Reighart, R.R.: ¬The WordSmith Indexing System (2001) 0.02 
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  16. Wright, L.W.; Nardini, H.K.G.; Aronson, A.R.; Rindflesch, T.C.: Hierarchical concept indexing of full-text documents in the Unified Medical Language System Information sources Map (1999) 0.02 
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    Abstract
    Full-text documents are a vital and rapidly growing part of online biomedical information. A single large document can contain as much information as a small database, but normally lacks the tight structure and consistent indexing of a database. Retrieval systems will often miss highly relevant parts of a document if the document as a whole appears irrelevant. Access to full-text information is further complicated by the need to search separately many disparate information resources. This research explores how these problems can be addressed by the combined use of 2 techniques: 1) natural language processing for automatic concept-based indexing of full text, and 2) methods for exploiting the structure and hierarchy of full-text documents. We describe methods for applying these techniques to a large collection of full-text documents drawn from the Health Services / Technology Assessment Text (HSTAT) database at the NLM and examine how this hierarchical concept indexing can assist both document- and source-level retrieval in the context of NLM's Information Source Map project
  17. 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
  18. 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
  19. Somers, H.: Example-based machine translation : Review article (1999) 0.02 
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Authors

Years

Languages

  • e 78
  • d 21
  • da 1
  • f 1
  • m 1
  • More… Less…

Types

  • a 82
  • m 9
  • el 8
  • s 4
  • p 3
  • x 2
  • b 1
  • d 1
  • More… Less…

Themes

Subjects

Classifications