Aljoscha Burchardt

Learn More
This paper describes the SALSA corpus, a large German corpus manually annotated with role-semantic information, based on the syntactically annotated TIGER newspaper corpus (Brants et al., 2002). The first release, comprising about 20,000 annotated predicate instances (about half the TIGER corpus), is scheduled for mid-2006. In this paper we discuss the(More)
This paper discusses our contribution to the third RTE Challenge – the SALSA RTE system. It builds on an earlier system based on a relatively deep linguistic analysis, which we complement with a shallow component based on word overlap. We evaluate their (combined) performance on various data sets. However, earlier observations that the combination of(More)
We present a pilot study on an evaluation method which is able to rank translation outputs with no reference translation, given only their source sentence. The system employs a statistical classifier trained upon existing human rankings, using several features derived from analysis of both the source and the target sentences. Development experiments on one(More)
Future improvement of machine translation systems requires reliable automatic evaluation and error classification measures to avoid time and money consuming human classification. In this article, we propose a new method for automatic error classification and systematically compare its results to those obtained by humans. We show that the proposed automatic(More)
Current metrics for evaluating machine translation quality have the huge drawback that they require human-quality reference translations. We propose a truly automatic evaluation metric based on IBM1 lexicon probabilities which does not need any reference translations. Several variants of IBM1 scores are systematically explored in order to find the most(More)