-
Sebastiani, F.: ¬A tutorial an automated text categorisation (1999)
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- Abstract
- The automated categorisation (or classification) of texts into topical categories has a long history, dating back at least to 1960. Until the late '80s, the dominant approach to the problem involved knowledge-engineering automatic categorisers, i.e. manually building a set of rules encoding expert knowledge an how to classify documents. In the '90s, with the booming production and availability of on-line documents, automated text categorisation has witnessed an increased and renewed interest. A newer paradigm based an machine learning has superseded the previous approach. Within this paradigm, a general inductive process automatically builds a classifier by "learning", from a set of previously classified documents, the characteristics of one or more categories; the advantages are a very good effectiveness, a considerable savings in terms of expert manpower, and domain independence. In this tutorial we look at the main approaches that have been taken towards automatic text categorisation within the general machine learning paradigm. Issues of document indexing, classifier construction, and classifier evaluation, will be touched upon.
-
Boleda, G.; Evert, S.: Multiword expressions : a pain in the neck of lexical semantics (2009)
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- Abstract
- Mit einem Überblick über: Probleme, Methoden, Stand der Forschung u. Literatur.
- Date
- 1. 3.2013 14:56:22
-
Snajder, J.: Distributional semantics of multi-word expressions (2013)
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- Content
- Folien einer Präsentation anlässlich COST Action IC1207 PARSEME Meeting, Warsaw, September 16, 2013. Vgl. den Beitrag: Snajder, J., P. Almic: Modeling semantic compositionality of Croatian multiword expressions. In: Informatica. 39(2015) H.3, S.301-309.
- Date
- 29. 4.2016 12:04:50
-
Snajder, J.; Almic, P.: Modeling semantic compositionality of Croatian multiword expressions (2015)
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- Date
- 29. 4.2016 12:42:17
-
Griffiths, T.L.; Steyvers, M.: ¬A probabilistic approach to semantic representation (2002)
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- Date
- 29. 6.2015 14:55:01
29. 6.2015 16:09:05
-
Rindflesch, T.C.; Aronson, A.R.: Semantic processing in information retrieval (1993)
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- Date
- 29. 6.2015 14:51:28
-
Stoykova, V.; Petkova, E.: Automatic extraction of mathematical terms for precalculus (2012)
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- Date
- 29. 5.2012 10:17:08
-
Ramisch, C.; Schreiner, P.; Idiart, M.; Villavicencio, A.: ¬An evaluation of methods for the extraction of multiword expressions (20xx)
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Lund, B.D.: ¬A chat with ChatGPT : how will AI impact scholarly publishing? (2022)
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Spitkovsky, V.I.; Chang, A.X.: ¬A cross-lingual dictionary for english Wikipedia concepts (2012)
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- Content
- Vgl. auch: Spitkovsky, V., P. Norvig: From words to concepts and back: dictionaries for linking text, entities and ideas. In: http://googleresearch.blogspot.de/2012/05/from-words-to-concepts-and-back.html. Für den Datenpool vgl.: nlp.stanford.edu/pubs/corsswikis-data.tar.bz2.
-
Hausser, R.: Language and nonlanguage cognition (2021)
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Shree, P.: ¬The journey of Open AI GPT models (2020)
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Lund, B.D.: ¬A brief review of ChatGPT : its value and the underlying GPT technology (2023)
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Brown, T.B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D.M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; Amodei, D.: Language models are few-shot learners (2020)
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Zhai, X.: ChatGPT user experience: : implications for education (2022)
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Park, J.S.; O'Brien, J.C.; Cai, C.J.; Ringel Morris, M.; Liang, P.; Bernstein, M.S.: Generative agents : interactive simulacra of human behavior (2023)
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Spitkovsky, V.; Norvig, P.: From words to concepts and back : dictionaries for linking text, entities and ideas (2012)
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Aydin, Ö.; Karaarslan, E.: OpenAI ChatGPT generated literature review: : digital twin in healthcare (2022)
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