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Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing
Learning from texts — A terminological metareasoning perspective
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Author(s):
Udo Hahn
,
Manfred Klenner
,
Klemens Schnattinger
Publication date
(Online):
June 7 2005
Publisher:
Springer Berlin Heidelberg
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Reification: Reflection without metaphysics
Daniel P. Friedman
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Mitchell Wand
(1984)
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The KL-ONE family
William Woods
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James Schmolze
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Representing reified relations in Loom
ROBERT MACGREGOR
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Publication date (Print):
1996
Publication date (Online):
June 7 2005
Pages
: 453-468
DOI:
10.1007/3-540-60925-3_66
SO-VID:
424da62d-8189-4ec8-972f-ab2c6e6442ee
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Book chapters
pp. 1
Learning approaches for natural language processing
pp. 17
Separating learning and representation
pp. 33
Natural language grammatical inference: A comparison of recurrent neural networks and machine learning methods
pp. 48
Extracting rules for grammar recognition from Cascade-2 networks
pp. 61
Generating English plural determiners from semantic representations: A neural network learning approach
pp. 75
Knowledge acquisition in concept and document spaces by using self-organizing neural networks
pp. 87
Using hybrid connectionist learning for speech/language analysis
pp. 102
SKOPE: A connectionist/symbolic architecture of spoken Korean processing
pp. 117
Integrating different learning approaches into a multilingual spoken language translation system
pp. 132
Learning language using genetic algorithms
pp. 146
A statistical syntactic disambiguation program and what it learns
pp. 160
Training stochastic grammars on semantical categories
pp. 173
Learning restricted probabilistic link grammars
pp. 188
Learning PP attachment from corpus statistics
pp. 203
A minimum description length approach to grammar inference
pp. 217
Automatic classification of dialog acts with Semantic Classification Trees and Polygrams
pp. 230
Sample selection in natural language learning
pp. 246
Learning information extraction patterns from examples
pp. 261
Implications of an automatic lexical acquisition system
pp. 275
Using learned extraction patterns for text classification
pp. 290
Issues in inductive learning of domain-specific text extraction rules
pp. 302
Applying machine learning to anaphora resolution
pp. 315
Embedded machine learning systems for natural language processing: A general framework
pp. 329
Acquiring and updating hierarchical knowledge for machine translation based on a clustering technique
pp. 343
Applying an existing machine learning algorithm to text categorization
pp. 355
Comparative results on using inductive logic programming for corpus-based parser construction
pp. 370
Learning the past tense of English verbs using inductive logic programming
pp. 385
A dynamic approach to paradigm-driven analogy
pp. 399
Can punctuation help learning?
pp. 413
Using parsed corpora for circumventing parsing
pp. 425
A symbolic and surgical acquisition of terms through variation
pp. 439
A revision learner to acquire verb selection rules from human-made rules and examples
pp. 453
Learning from texts — A terminological metareasoning perspective
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