Applied multivariate statistical analysis
Applied multivariate statistical analysis
C4.5: programs for machine learning
C4.5: programs for machine learning
The acquisition and use of context-dependent grammars for English
Computational Linguistics
Hypothesis-Driven Constructive Induction in AQ17-HCI: A Method and Experiments
Machine Learning - Special issue on evaluating and changing representation
Inducing deterministic Prolog parsers from treebanks: a machine learning approach
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Learning text analysis rules for domain-specific natural language processing
Learning text analysis rules for domain-specific natural language processing
Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing
Text processing without a priori domain knowledge: semi-automatic linguistic analysis for incremental knowledge acquisition
Applying explanation-based learning to control and speeding-up natural language generation
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Learning parse and translation decisions from examples with rich context
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Automatic grammar induction and parsing free text: a transformation-based approach
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Statistical decision-tree models for parsing
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Grammar specialization through entropy thresholds
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Cue phrase classification using machine learning
Journal of Artificial Intelligence Research
Using decision trees for conference resolution
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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Any large language processing software relies in its operation on heuristic decisions concerning the strategy of processing. These decisions are usually "hard-wired" into the software in the form of hand-crafted heuristic rules, independent of the nature of the processed texts. We propose an alternative, adaptive approach in which machine learning techniques learn the rules from examples of sentences in each class. We have experimented with a variety of learning techniques on a representative instance of this problem within the realm of parsing. Our approach lead to the discovery of new heuristics that perform significantly better than the current hand-crafted heuristic. We discuss the entire cycle of application of machine learning and suggest a methodology for the use of machine learning as a technique for the adaptive optimisation of language-processing software.