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This paper deals with the creation of the first morphological treebank for German by merging two pre-existing linguistic databases. The first of these is the linguistic database CELEX which is a standard resource for German morphology. We build on its refurbished and modernized version. The second resource is GermaNet, a lexical-semantic network which also provides partial markup for compounds. We describe the state of the art and the essential characteristics of both databases and our latest revisions. As the merging involves two data sources with distinct annotation schemes, the derivation of the morphological trees for the unified resource is not trivial. We discuss how we overcome problems with the data and format, in particular how we deal with overlaps and complementary scopes. The resulting database comprises about 100,000 trees whose format can be chosen according to the requirements of the application at hand. In our discussion, we show some future directions for morphological treebanks. The Perl script for the generation of the data from the sources will be made publicly available on our website.
We present a method to identify and document a phenomenon on which there is very little empirical data: German phrasal compounds occurring in the form of as a single token (without punctuation between their components). Relying on linguistic criteria, our approach implies to have an operational notion of compounds which can be systematically applied as well as (web) corpora which are large and diverse enough to contain rarely seen phenomena. The method is based on word segmentation and morphological analysis, it takes advantage of a data-driven learning process. Our results show that coarse-grained identification of phrasal compounds is best performed with empirical data, whereas fine-grained detection could be improved with a combination of rule-based and frequency-based word lists. Along with the characteristics of web texts, the orthographic realizations seem to be linked to the degree of expressivity.
We present a quantitative approach to disambiguating flat morphological analyses and producing more deeply structured analyses. Based on existing morphological segmentations, possible combinations of resulting word trees for the next level are filtered first by criteria of linguistic plausibility and then by weighting procedures based on the geometric mean. The frequencies for weighting are derived from three different sources (counts of morphs in a lexicon, counts of largest constituents in a lexicon, counts of token frequencies in a corpus) and can be used either to find the best analysis on the level of morphs or on the next higher constituent level. The evaluation shows that for this task corpus-based frequency counts are slightly superior to counts of lexical data.