Walter Daelemans,比利时安特卫普大学教授。Antal van den Bosch,荷兰蒂尔堡大学教授。
目錄：
导读
Preface
1 Memory-Based Learning in Natural Language Processing
1.1 Natural language processing as classification
1.2 A linguistic example
1.3 Roadmap and software
1.4 Fiirther reading
2 Inspirations from linguistics and artificial intelligence
2.1 Inspirations from linguistics
2.2 Inspirations from artificial intelligence
2.3 Memory-based language processing literature
2.4 Conclusion
3 Memory and Similarity
3.1 German plural formation
3.2 Similarity metric
3.2. 1 Information-theoretic feature weighting .
3.2.2 Alternative feature weighting methods
3.2.3 Getting started with TiMBL
3.2.4 Feature weighting in TiMBL
3.2.5 Modified value difference metric
3.2.6 Value clustering in TiMBL
3.2.7 Distance-weighted class voting
3.2.8 Distance-weighted class voting in TiMBL
3.3 Analyzing the output of MBLP
3.3.1 Displaying nearest neighbors in TiMBL
3.4 Implementation issues
3.4.1 TiMBL trees
3.5 Methodology
3.5.1 Experimental methodology in TiMBL
3.5.2 Additional performance measures in TiMBL
3.6 Conclusion
4 Application to morpho-phonology
4.1 Phonemization
4.1.1 Memory-based word phonemization
4.1.2 TreeTalk
4.1.3 IGTree in TiMBL
4.1.4 Experiments: applying IGTree to word phonemization
4.1.5 TRIBL: trading memory for speed
4.1.6 TRIBL in TiMBL examples Editing
4.2 Morphological analysis
4.2.1 Dutch morphology
4.2.2 Feature and class encoding
4.2.3 Experiments: MBMA on Dutch wordforms
4.3 Conclusion
5 Application to shallow parsing
5.1 Part-of-speech tagging
5.1.1 Memory-based tagger architecture
5.1.2 Results
5.2 Constituent chunking
5.2.1 Results
5.2.2 Using Mbt and Mbtg for chunking
5.3 Relation finding
5.3.1 Relation finder architecture
5.3.2 Results
5.4 Conclusion
6 Abstraction and generalization
6.1 Lazy versus eager learning
6.1.1 Benchmark language learning tasks
6.1.2 Forgetting by rule induction is harmful in language learning
6.2 Editing
6.3 Why forgetting examples can be harmful
6.4 Generalizing examples
6.4.1 Careful abstraction in memory-based learning
6.4.2 Getting started with FAMBL
6.4.3 Experiments with FAMBL
6.5 Conclusion
6.6 Further reading
7 Extensions
7.1 Wrapped progressive sampling
7.1.1 The wrapped progressive sampling algorithm
7.1.2 Getting started with wrapped progressive sampling
7.1.3 Wrapped progressive sampling results
7.2 Optimizing output sequences
7.2.1 Stacking
7.2.2 Predicting class n-grams
7.2.3 Combining stacking and class n-grams
7.2.4 Summary
7.3 Conclusion
7.4 Further reading
Bibliography
Index
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