PhD thesis - School of Informatics - University of Edinburgh

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List of Figures 2.1 Top ten internet languages . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Orthographical variants in Der Spiegel between 1994 and 2000 . . . . 13 2.3 Onysko’s classification of the term anglicism . . . . . . . . . . . . . 17 2.4 Splitting options for a mixed-lingual compound . . . . . . . . . . . . 20 2.5 Hinglish song lyrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.6 Mixed-lingual German/English analyser . . . . . . . . . . . . . . . . 38 2.7 Mixed-lingual analyser output . . . . . . . . . . . . . . . . . . . . . 39 2.8 Hidden Markov Model architecture . . . . . . . . . . . . . . . . . . . 42 3.1 System architecture of the English inclusion classifier . . . . . . . . . 55 3.2 Yahoo queries with different language preferences . . . . . . . . . . . 62 3.3 Performance increase of the corpus search module with access to in- creasing corpus sub-sets . . . . . . . . . . . . . . . . . . . . . . . . 76 3.4 Learning curve of a supervised machine learning classifier versus the performance of the annotation-free English inclusion classifier . . . . 97 4.1 Time spent on system extension . . . . . . . . . . . . . . . . . . . . 102 4.2 Extended system architecture . . . . . . . . . . . . . . . . . . . . . . 106 5.1 Example parse tree of a German TIGER sentence containing an En- glish inclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 5.2 Sentence length distribution of the inclusion set and a completely ran- dom TIGER data set . . . . . . . . . . . . . . . . . . . . . . . . . . 120 5.3 Parse tree with local rule probabilities . . . . . . . . . . . . . . . . . 122 5.4 Tree transformation for a coordinated noun phrase rule . . . . . . . . 123 5.5 Tree transformation employed in the inclusion entity parser . . . . . . 126 ix
5.6 Average relative token frequencies for sentences of equal length . . . 130 5.7 Partial parse trees of produced by the baseline parser, found in the gold standard and output by the inclusion entity model . . . . . . . . . . . 136 5.8 Partial parse of the inclusion entity model for a false positive inclusion 137 5.9 Complete c- and f-structures for an English example sentence . . . . . 140 x
Page 1 and 2: Automatic Detection of English Incl
Page 3 and 4: these parsers with the annotation-f
Page 5 and 6: Declaration I declare that this the
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Page 15 and 16: Chapter 1. Introduction 2 siderable
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Chapter 3. Tracking English Inclusi
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Chapter 4 System Extension to a New
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Chapter 4. System Extension to a Ne
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Chapter 5 Parsing English Inclusion
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Chapter 5. Parsing English Inclusio
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Chapter 6 Other Potential Applicati
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Chapter 6. Other Potential Applicat
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Chapter 7 Conclusions and Future Wo
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Chapter 7. Conclusions and Future W
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Appendix A. Evaluation Metrics and
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Appendix B. Guidelines for Annotati
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Appendix C TIGER Tags and Labels C.
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Appendix C. TIGER Tags and Labels 1
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Appendix C. TIGER Tags and Labels 1
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Bibliography 198 Andersen, G. (2005
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Bibliography 200 Bresnan, J. (2001)
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Bibliography 202 Damashek, M. (1995
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Bibliography 204 Finkel, J., Dingar
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Bibliography 206 Hachey, B., Alex,
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Bibliography 208 Kirkness, A. (1984
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Bibliography 210 and Technology (In
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Bibliography 212 Poplack, S. (1988)
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Bibliography 214 Sokol, D. K. (2000
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Bibliography 216 Yang, W. (1990). A
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PhD thesis - School of Informatics - University of Edinburgh

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