An Unsupervised Semantic Tagger Applied to German

Paul Buitelaar, Jan Alexandersson, Tilman Jaeger, Stephan Lesch, Norbert Pfleger, Diana Raileanu
DFKI GmbH
Stuhlsatzenhausweg 3
D66123 Saarbruecken, Germany
{paulb,janal,jaeger,lesch,pfleger,raileanu}@dfki.de
Tanja von den Berg, Kerstin Klckner, Holger Neis, Hubert Schlarb
Department of Computational Linguistics,
Universitt des Saarlandes
Postfach 151150
66041 Saarbrcken, Germany
{kekl,hone,husc}@coli.unisb.de

Abstract
We describe an unsupervised
semantic tagger, applied to German,
but which could be used with any
language for which a corresponding
"XNet" (WordNet, GermaNet, etc.),
POS tagger and morphological
analyzer are available.
Disambiguation is performed by
comparing cooccurrence weights on
pairs of semantic classes (synsets
from GermaNet). Precision is around
67% at a recall of around 65% (for all
ambiguous words  81% for all words
at a recall of 80%). Our results show
the influence of context size and of
semantic class frequency in the
training corpus.

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