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The paper investigates the origins of the German/Dutch particle toch/doch) in the hope of shedding light on a puzzle with respect to doch/toch and to shed some light on two theoretical issues. The puzzle is the nearly opposite meaning of the stressed and unstressed versions of the particle which cannot be accounted for in standard theories of the meaning of stress. One theoretical issue concerns the meaning of stress: whether it is possible to reduce the semantic contribution of a stressed item to the meaning of the item and the meaning of stress. The second issue is whether the complex use of a particle like doch/toch can be seen as an instance of spread or whether it has to be seen as having a core meaning which is differentiated by pragmatics operating in different contexts.
We use the etymology of doch and doch as to+u+h (that+ question marker+ emphatic marker) to argue for an origin as a question tag checking a hearer opinion. Stress on the tag indicates an opposite opinion (of the common ground or the speaker) and this sets apart two groups of uses spreading in different directions. This solves the puzzle, indicates that the assumption of spread is useful and offers a subtle correction of the interpretation of stress. While stress always means contrast with a contrasting item, if the particle use is due to spread, it is not guaranteed that the unstressed particle has a corresponding use (or inversely).
Parsing coordinations
(2009)
The present paper is concerned with statistical parsing of constituent structures in German. The paper presents four experiments that aim at improving parsing performance of coordinate structure: 1) reranking the n-best parses of a PCFG parser, 2) enriching the input to a PCFG parser by gold scopes for any conjunct, 3) reranking the parser output for all possible scopes for conjuncts that are permissible with regard to clause structure. Experiment 4 reranks a combination of parses from experiments 1 and 3. The experiments presented show that n- best parsing combined with reranking improves results by a large margin. Providing the parser with different scope possibilities and reranking the resulting parses results in an increase in F-score from 69.76 for the baseline to 74.69. While the F-score is similar to the one of the first experiment (n-best parsing and reranking), the first experiment results in higher recall (75.48% vs. 73.69%) and the third one in higher precision (75.43% vs. 73.26%). Combining the two methods results in the best result with an F-score of 76.69.