PhD thesis - School of Informatics - University of Edinburgh

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Chapter 3. Tracking English Inclusions in German 67 tify the English inclusions in the utterance. In this case, all tokens are unambiguous and therefore no further post-processing is required. Security-Tool verhindert, dass Hacker über Google Sicherheitslücken finden. Translation: Security Tool prevents hackers from finding security holes via Google. The second example is part of a quote made by fashion designer Jil Sander in an interview with FAZ (FAZ-Magazin, 1996). These words have become the prime example of anglicisation of German for which she was the first to receive the title “Language Diluter of the Year” from the Verein Deutscher Sprache e.V. (German language asso- ciation) in 1997. This example contains numerous English inclusions, most of which are identified by the lexicon module (contemporary, Future, Concept, Collection and Audience). The tokens Tailored, coordinated and supported are correctly classified as English by the search engine module. The only ambiguous tokens are Future and Hand. They are resolved in the post-processing module on the basis of context. Ich habe verstanden, daß man contemporary sein muß, daß man Future-Denken haben muß. Meine Idee war, die Hand-Tailored-Geschichte mit neuen Technologien zu verbinden. Und für den Erfolg war mein coordinated Concept entscheidend, die Idee, daß man viele Teile einer Collection miteinander combinen kann. Aber die Audience hat das alles von Anfang an auch supported. Translation: I understood that one has to be contemporary, that one has to have future thinking. My idea was to combine the hand-tailored story with new technologies. And crucial to the success was my coordinated concept that one can combine parts of a collection. But the audience has supported this from the beginning as well.
Chapter 3. Tracking English Inclusions in German 68 3.4 Evaluation and Analysis This section first evaluates the performance of the English inclusion classifier on the German development data for each domain and subsequently examines the performance of individual system modules. Finally, the performance of the classifier on a random selection of unseen test data and another new data set provided by Marcadet et al. (2005) is reported. The latter allows comparison with another state-of-the-art mixed-lingual LID approach. 3.4.1 Evaluation of the Tool Output The identification of English inclusions is similar to named entity recognition (NER) but on single tokens. The classifier’s performance is therefore evaluated against the gold standard in terms of accuracy for all tokens, and balanced F-score (the harmonic mean of precision and recall) for target and predicted English tokens. Both metrics are defined in Appendix A.1. Baseline accuracy scores shown in Table 3.8 are determined assuming that the system found none of the English tokens in the data and believes that all tokens are German. As precision, recall and F-score are calculated in relation to the English tokens in the gold standard, they are essentially zero for the baseline. For this reason, only accuracy baseline scores (and not F-score baseline scores) are reported. Unsurprisingly, the baseline accuracies are relatively high as most tokens in a German text are German and the amount of foreign material is relatively small. The full system, the combined lexicon lookup and search engine modules as well as post-processing and document consistency checking, yields relatively high F-scores of 84.37 and 91.35 for the internet and space travel data but only a low F-score of 66.67 for the EU data. The latter is due to the sparseness of English inclusions in that domain (see Table 3.1 in Section 3.2). Although the recall for this data (76.19%) is compara- ble to that of the other two domains, the number of false positives is high, causing low precision and F-score. Results were compared using the chi square (χ 2 ) test (see Ap- pendix A.4.1). It shows that the additional classification of English inclusions yields highly statistically significant improvements (d f = 1, p ≤ 0.001) in accuracy over the baseline of 4.30% for the internet data and 2.46% for the space travel data. When clas- sifying English inclusions in the EU data, accuracy increases only slightly by 0.09% which is not statistically significant (d f = 1, p ≤ 1).
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Automatic Detection of English Incl
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these parsers with the annotation-f
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Declaration I declare that this the
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3.3.5 Post-processing Module . . .
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A.2.2 Kappa Coefficient . . . . . .
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5.6 Average relative token frequenc
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3.16 Most frequent English inclusio
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Chapter 1. Introduction 2 siderable
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Chapter 1. Introduction 4 Chapter 3
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Chapter 1. Introduction 6 1.1 Relat
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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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