PhD thesis - School of Informatics - University of Edinburgh

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Chapter 3. Tracking English Inclusions in German 53 3.2.4 Annotation Issues Although the aforementioned annotation guidelines are relatively clear, the actual annotation revealed some tricky cases which were difficult to classify with the binary classification scheme described above. This section discusses the main issues which need to be clarified for revising and possibly extending the current guidelines. These complicated instances mainly concern NEs which cannot be found in the individual lexicons but have certain language specific morphology and comprise character sequences typical for that language. Table 3.4 lists some examples for different types of NEs that stem from German, English and other language origins. Dudenhöffer and Neckermann are clearly German names, just as Hutchison and Forrester are En- glish names. Difficult cases are Sony (sonus + sonny), Activy (similar to activity) or Booxtra (book + extra). Such English-like examples were not annotated as English inclusions in the gold standard. Therefore, if the system identifies them as English, its performance scores determined in the evaluation (Section 3.4) are to some extent unfairly penalised. A way of including these instances in the evaluation is to annotate them as English with an attribute distinguishing them from real English words. NE type German English Other Person Dudenhöffer Hutchison Kinoshita Company Neckermann Forrester Kelkoo Table 3.4: Difficult annotation examples. The German development corpus also contains NEs from other languages. Readers might well know that Kinoshita is neither a German nor an English name, although they may not be able to identify it as a Japanese name. Interestingly, Font-Llitjós and Black (2001) show that out of a list of 516 names, only 43% can be labelled confidently by human annotators with respect to their language origin. An example in our corpus where the language origin is not at all apparent in the character sequence is the name Kelkoo, a play on words derived from the French for What a bargain (Quel coup). It represents the English phonetic spelling of the French phrase. Other foreign names are Toshiba (Japanese), Svanberg (Swedish), Altavista (Spanish) and Centrino (Italian).
Chapter 3. Tracking English Inclusions in German 54 As yet, such instances are also annotated as O, i.e. not English. Entities from other languages could instead be annotated as INTERNATIONAL. Another issue that arises is the annotation of place names. According to the annotation guidelines, English place names are not annotated as English inclusions unless they have a German equivalent which is generally used instead. So, for example, the location Munich would be annotated because the German equivalent München is generally the name preferred by German speakers. Examples of English location names found in the corpus that were not annotated are Virginia, Houston or New York. It can be argued that similar to the examples above they have certain English characteristics and sometimes coincide with common English words in the lexicon, as is the case for Bath. Identifying such examples, will be particularly advantageous for certain TTS applications such as car navigation systems where the correct pronunciation of place names is vital. 3.3 English Inclusion Classifier: System Overview This section presents the annotation-free English inclusion classifier developed as part of this thesis project. It identifies English inclusions in German text by means of computationally inexpensive lexicon lookup and web search procedures. Annotation- free means that the system does not require any annotated training data and only relies on lexicons and the World Wide Web. This system allows linguists and lexicographers to observe language changes over time, and to investigate the use and frequency of foreign words in a given language and domain. The output also represents valuable information for a number of applications, including parsing, polyglot TTS synthesis and MT. The initial system was built for a preliminary study to examine the frequency of English inclusions in German newspaper text on different subjects and to gain a better understanding of how to recognise such instances automatically. The system described in this section represents the final version which was developed on the basis of error analysis and numerous parameter tuning experiments. These fine-tuning experiments are described in detail in Section 3.5.
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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 5 Parsing English Inclusion
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Chapter 6 Other Potential Applicati
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Chapter 7 Conclusions and Future Wo
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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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