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Corpus-based identification and disambiguation of reading indicators for German nominalizations
(2010)
Corpus data is often structurally and lexically ambiguous; corpus extraction methodologies thus must be made aware of ambiguities. Therefore, given an extraction task, all relevant ambiguities must be identified. To resolve these ambiguities, contextual data responsible for one or another reading is to be considered. In the context of our present work, German -ung-nominalizations and their sortal readings are under examination. A number of these nominalizations may be read as an event or a result, depending on the semantic group they belong to. Here, we concentrate on nominalizations of verbs of saying (henceforth: "verba dicendi"), identify their context partners and their influence on the sortal reading of the nominalizations in question. We present a tool which calculates the sortal reading of such nominalizations and thus may improve not only corpus extraction, but also e.g. machine translation. Lastly, we describe successful attempts to identify the correct sortal reading, conclusions and future work.
So far, Sepedi negations have been considered more from the point of view of lexicographical treatment. Theoretical works on Sepedi have been used for this purpose, setting as an objective a neat description of these negations in a (paper) dictionary. This paper is from a different perspective: instead of theoretical works, corpus linguistic methods are used: (1) a Sepedi corpus is examined on the basis of existing descriptions of the occurrences of a relevant verb, looking at its negated forms from a purely prescriptive point of view; (2) a "corpus-driven" strategy is employed, looking only for sequences of negation particles (or morphemes) in order to list occurring constructions, without taking into account the verbs occurring in them, apart from their endings. The approach in (2) is only intended to show a possible methodology to extend existing theories on occurring negations. We would also like to try to help lexicographers to establish a frequency-based order of entries of possible negation forms in their dictionaries by showing them the number of respective occurrences. As with all corpus linguistic work, however, we must regard corpus evidence not as representative, but as tendencies of language use that can be detected and described. This is especially true for Sepedi, for which only few and small corpora exist. This paper also describes the resources and tools used to create the necessary corpus and also how it was annotated with part of speech and lemmas. Exploring the quality of available Sepedi part-of-speech taggers concerning verbs, negation morphemes and subject concords may be a positive side result.
This paper describes the application of probabilistic part of speech taggers to the Dzongkha language. A tag set containing 66 tags is designed, which is based on the Penn Treebank. A training corpus of 40,247 tokens is utilized to train the model. Using the lexicon extracted from the training corpus and lexicon from the available word list, we used two statistical taggers for comparison reasons. The best result achieved was 93.1% accuracy in a 10-fold cross validation on the training set. The winning tagger was thereafter applied to annotate a 570,247 token corpus.