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Hits 1 – 12 of 12

1
Learning Subjective Language
In: Computational linguistics. - Cambridge, Mass. : MIT Press 30 (2004) 3, 277-308
OLC Linguistik
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2
Learning subjective language
In: Computational linguistics. - Cambridge, Mass. : MIT Press 30 (2004) 3, 277-308
BLLDB
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3
Theory, method, and practice in computer content analysis
Bruce, Rebecca F. (Mitarb.); Arian, Gretchen W. (Mitarb.); Diefenbach, Donald L. (Mitarb.). - Westport, Conn. [u.a.] : Ablex Publ., 2001
BLLDB
UB Frankfurt Linguistik
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4
Semantic tagging
Palmer, Martha Stone (Hrsg.); Light, Marc (Hrsg.); Resnik, Philip (Mitarb.)...
In: Natural language engineering. - Cambridge : Cambridge University Press 5 (1999) 2, 113-218
BLLDB
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5
Articles - Decomposable Modeling in Natural Language Processing
In: Computational linguistics. - Cambridge, Mass. : MIT Press 25 (1999) 2, 195-208
OLC Linguistik
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6
Decomposable modeling in natural language processing
In: Computational linguistics. - Cambridge, Mass. : MIT Press 25 (1999) 2, 195-207
BLLDB
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7
Corpus-Based Methods in Language and Speech Processing
In: Computational linguistics. - Cambridge, Mass. : MIT Press 24 (1998) 2, 317-318
OLC Linguistik
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8
A statistical decision making method : a case study on prepositional phrase attachment
In: Computational natural language learning. - Somerset, NJ : Association for Computational Linguistics (1997), 33-42
BLLDB
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9
Distinguishing Word Senses in Untagged Text ...
Pedersen, Ted; Bruce, Rebecca. - : arXiv, 1997
BASE
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10
Word-Sense Disambiguation Using Decomposable Models ...
Bruce, Rebecca; Wiebe, Janyce. - : arXiv, 1994
Abstract: Most probabilistic classifiers used for word-sense disambiguation have either been based on only one contextual feature or have used a model that is simply assumed to characterize the interdependencies among multiple contextual features. In this paper, a different approach to formulating a probabilistic model is presented along with a case study of the performance of models produced in this manner for the disambiguation of the noun "interest". We describe a method for formulating probabilistic models that use multiple contextual features for word-sense disambiguation, without requiring untested assumptions regarding the form of the model. Using this approach, the joint distribution of all variables is described by only the most systematic variable interactions, thereby limiting the number of parameters to be estimated, supporting computational efficiency, and providing an understanding of the data. ... : 8 pages, Unix compressed, uuencoded Postscript file ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/cmp-lg/9406005
https://dx.doi.org/10.48550/arxiv.cmp-lg/9406005
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11
Computerlinguistische und sprachtechnologische Untersuchungen
Heyer, Gerhard (Mitarb.); Gross, Maurice (Mitarb.); Kanngießer, Siegfried (Mitarb.)...
In: Theorie und Praxis des Lexikons. - Berlin [u.a.] : de Gruyter (1993), 207-348
BLLDB
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12
Is there content in empty heads?
In: COLING-90. Vol. 3. - Helsinki : Univ. of Helsinki (1990), 138-143
BLLDB
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