14. IJCAI 1995: Montréal, Québec, Canada - Learning for Natural Language Processing
Stefan Wermter, Ellen Riloff, Gabriele Scheler (Eds.): Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing. Springer 1996 Lecture Notes in Computer Science ISBN 3-540-60925-3- Stefan Wermter, Ellen Riloff, Gabriele Scheler: Learning approaches for natural language processing. 1-16
Connectionist Networks and Hybrid Approaches
- Steve Lawrence, Sandiway Fong, C. Lee Giles: Natural language grammatical inference: a comparison of recurrent neural networks and machine learning methods. 33-47
- Ross Hayward, Alan B. Tickle, Joachim Diederich: Extracting rules for grammar recognition from Cascade-2 networks. 48-60
- Gabriele Scheler: Generating English plural determiners from semantic representations: a neural network learning approach. 61-74
- Werner Winiwarter, Erich Schweighofer, Dieter Merkl: Knowledge acquisition in concept and document spaces by using self-organizing neural networks. 75-86
- Volker Weber, Stefan Wermter: Using hybrid connectionist learning for speech/language analysis. 87-101
- Gary Geunbae Lee, Jong-Hyeok Lee: SKOPE: A connectionist/symbolic architecture of spoken Korean processing. 102-116
- Petra Geutner, Bernhard Suhm, Finn Dag Buø, Thomas Kemp, Laura Mayfield, Arthur E. McNair, Ivica Rogina, Tanja Schultz, Tilo Sloboda, Wayne H. Ward, Monika Woszczyna, Alex Waibel: Integrating different learning approaches into a multilingual spoken language translation system. 117-131
Statistical Approaches
- Murat Ersan, Eugene Charniak: A statistical syntactic disambiguation program and what it learns. 146-159
- Alexander Franz: Learning PP attachment from corpus statistics. 188-202
- Peter Grünwald: A minimum description length approach to grammar inference. 203-216
- Marion Mast, Heinrich Niemann, Elmar Nöth, Ernst Günter Schukat-Talamazzini: Automatic classification of dialog acts with semantic classification trees and polygrams. 217-229
Symbolic Approaches
- Scott B. Huffman: Learning information extraction patterns from examples. 246-260
- Peter M. Hastings: Implications of an automatic lexical acquisition system. 261-274
- Ellen Riloff: Using learned extraction patterns for text classification. 275-289
- Stephen Soderland, David Fisher, Jonathan Aseltine, Wendy G. Lehnert: Issues in inductive learning of domain-specific text extraction rules. 290-301
- Claire Cardie: Embedded machine learning systems for natural language processing: a general framework. 315-328
- Takefumi Yamazaki, Michael J. Pazzani, Christopher J. Merz: Acquiring and updating hierarchical knowledge for machine translation based on a clustering technique. 329-342
- Isabelle Moulinier, Jean-Gabriel Ganascia: Applying an existing machine learning algorithm to text categorization. 343-354
- John M. Zelle, Raymond J. Mooney: Comparative results on using inductive logic programming for corpus-based parser construction. 355-369
- Raymond J. Mooney, Mary Elaine Califf: Learning the past tense of English verbs using inductive logic programming. 370-384
- Stefano Federici, Vito Pirrelli, François Yvon: A dynamic approach to paradigm-driven analogy. 385-398
- Miles Osborne: Can punctuation help learning? 399-412
- Christian Jacquemin: A symbolic and surgical acquisition of terms through variation. 425-438
- Shigeo Kaneda, Hussein Almuallim, Yasuhiro Akiba, Megumi Ishii, Tsukasa Kawaoka: A revision learner to acquire verb selection rules from human-made rules and examples. 439-452
- Udo Hahn, Manfred Klenner, Klemens Schnattinger: Learning from texts - a terminological metareasoning perspective. 453-468
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last updated on 2013-06-19T201401 by the dblp team