Search (41 results, page 1 of 3)

  • × language_ss:"e"
  • × theme_ss:"Computerlinguistik"
  • × year_i:[2020 TO 2030}
  1. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.04 
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    Language
    e
    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  2. Collard, J.; Paiva, V. de; Fong, B.; Subrahmanian, E.: Extracting mathematical concepts from text (2022) 0.03 
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  3. Al-Khatib, K.; Ghosa, T.; Hou, Y.; Waard, A. de; Freitag, D.: Argument mining for scholarly document processing : taking stock and looking ahead (2021) 0.03 
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  4. Morris, V.: Automated language identification of bibliographic resources (2020) 0.02 
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    Date
    2. 3.2020 19:04:22
    Language
    e
  5. Pepper, S.: ¬The typology and semantics of binominal lexemes : noun-noun compounds and their functional equivalents (2020) 0.02 
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    Abstract
    The dissertation establishes 'binominal lexeme' as a comparative concept and discusses its cross-linguistic typology and semantics. Informally, a binominal lexeme is a noun-noun compound or functional equivalent; more precisely, it is a lexical item that consists primarily of two thing-morphs between which there exists an unstated semantic relation. Examples of binominals include Mandarin Chinese ?? (tielù) [iron road], French chemin de fer [way of iron] and Russian ???????? ?????? (zeleznaja doroga) [iron:adjz road]. All of these combine a word denoting 'iron' and a word denoting 'road' or 'way' to denote the meaning railway. In each case, the unstated semantic relation is one of composition: a railway is conceptualized as a road that is composed (or made) of iron. However, three different morphosyntactic strategies are employed: compounding, prepositional phrase and relational adjective. This study explores the range of such strategies used by a worldwide sample of 106 languages to express a set of 100 meanings from various semantic domains, resulting in a classification consisting of nine different morphosyntactic types. The semantic relations found in the data are also explored and a classification called the Hatcher-Bourque system is developed that operates at two levels of granularity, together with a tool for classifying binominals, the Bourquifier. The classification is extended to other subfields of language, including metonymy and lexical semantics, and beyond language to the domain of knowledge representation, resulting in a proposal for a general model of associative relations called the PHAB model. The many findings of the research include universals concerning the recruitment of anchoring nominal modification strategies, a method for comparing non-binary typologies, the non-universality (despite its predominance) of compounding, and a scale of frequencies for semantic relations which may provide insights into the associative nature of human thought.
    Language
    e
  6. 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) 0.01 
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    Date
    23.11.2023 19:07:22
    Language
    e
  7. Roose, K.: ¬The brilliance and weirdness of ChatGPT (2022) 0.00 
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  8. Metz, C.: ¬The new chatbots could change the world : can you trust them? (2022) 0.00 
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  9. Huge "foundation models" are turbo-charging AI progress : The world that Bert built (2022) 0.00 
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  10. Hofstadter, D.: Artificial neural networks today are not conscious (2022) 0.00 
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  11. Agüera y Arcas, B.: Artificial neural networks are making strides towards consciousness (2022) 0.00 
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  12. Harari, Y.N.: ¬[Yuval-Noah-Harari-argues-that] AI has hacked the operating system of human civilisation (2023) 0.00 
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  13. Zaitseva, E.M.: Developing linguistic tools of thematic search in library information systems (2023) 0.00 
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    Abstract
    Within the R&D program "Information support of research by scientists and specialists on the basis of RNPLS&T Open Archive - the system of scientific knowledge aggregation", the RNPLS&T analyzes the use of linguistic tools of thematic search in the modern library information systems and the prospects for their development. The author defines the key common characteristics of e-catalogs of the largest Russian libraries revealed at the first stage of the analysis. Based on the specified common characteristics and detailed comparison analysis, the author outlines and substantiates the vectors for enhancing search inter faces of e-catalogs. The focus is made on linguistic tools of thematic search in library information systems; the key vectors are suggested: use of thematic search at different search levels with the clear-cut level differentiation; use of combined functionality within thematic search system; implementation of classification search in all e-catalogs; hierarchical representation of classifications; use of the matching systems for classification information retrieval languages, and in the long term classification and verbal information retrieval languages, and various verbal information retrieval languages. The author formulates practical recommendations to improve thematic search in library information systems.
    Language
    e
  14. ChatGPT : Optimizing language models for dalogue (2022) 0.00 
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  15. Lund, B.D.: ¬A chat with ChatGPT : how will AI impact scholarly publishing? (2022) 0.00 
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  16. Xiang, R.; Chersoni, E.; Lu, Q.; Huang, C.-R.; Li, W.; Long, Y.: Lexical data augmentation for sentiment analysis (2021) 0.00 
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  17. Laparra, E.; Binford-Walsh, A.; Emerson, K.; Miller, M.L.; López-Hoffman, L.; Currim, F.; Bethard, S.: Addressing structural hurdles for metadata extraction from environmental impact statements (2023) 0.00 
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  18. Aizawa, A.; Kohlhase, M.: Mathematical information retrieval (2021) 0.00 
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  19. Chou, C.; Chu, T.: ¬An analysis of BERT (NLP) for assisted subject indexing for Project Gutenberg (2022) 0.00 
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  20. Hausser, R.: Grammatical disambiguation : the linear complexity hypothesis for natural language (2020) 0.00 
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