PhD thesis - School of Informatics - University of Edinburgh

Chapter 3. Tracking English Inclusions in German 64
Post-processing type Example
Ambiguous words Space Station Crew
Single letters E-mail
Currencies & Units Euro
Function words Friends of the Earth
Abbreviations Europäische Union (EU)
Person names Präsident Bush
Table 3.7: Different types of post-processing rules.
ule involve resolving language classification of ambiguous words, single letter tokens,
currencies and units of measurement, function words, abbreviations and person names.
Each type of post-processing is listed in Table 3.7 with an example and explained in
more detail in the following. Individual contributions of each type are presented in
Section 3.4.2.3. Most of the rules lead to improvements in performance for all of the
three domains and none of them deteriorate the scores.
As only the token and its POS tag but not its surrounding context are considered in
the lexicon module classification, it is difficult to identify the language of interlingual
homographs, tokens with the same spelling in both languages (e.g. Station). Therefore,
the majority of post-processing rules are designed to disambiguate such instances. For
example, if a language ambiguous token is preceded and followed by an English token,
then its is also likely to be of English origin (e.g. Space Station Crew versus macht
Station auf Sizilien). The post-processing module applies rules that disambiguate such
interlingual homographs based on their POS tag and contextual information.
Moreover, the module contains rules designed to flag single-character tokens cor-
rectly. These occur because the tokeniser is set up to split hyphenated compounds
like E-mail into three separate tokens (Section 3.3.2). The core system identifies the
language of tokens with a length of more than one character and therefore only recog-
nises mail as English in this example. The post-processing rule flags E as English as
well. Several additional rules deal with names of currencies and units of measurements
and prevent them from being mistaken as English inclusions. Furthermore, some rules
were designed to classify English function words as English.
As the core system classifies each token individually, a further post-processing