Cross-Lingual Annotation Projection Models for Role-Semantic by Eingereicht von Sebastian Pado
By Eingereicht von Sebastian Pado
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Extra info for Cross-Lingual Annotation Projection Models for Role-Semantic Information
These imbalances are problems which EUROPARL shares with many other, commonly used corpora such as the Penn Treebank (Marcus et al. 1993). , as opposed to noise-prone internetharvested corpora), and is commonly used for multilingual research such as statistical Machine Translation (Koehn 2005). In addition, we find that our application of EUROPARL – annotation projection of semantic class and role information – is comparatively robust to EUROPARL’s imbalances. In particular, the induction of semantic classes (Chapter 4) only requires an open domain corpus, which EUROPARL provides.
A high degree of concept-level parallelism is therefore crucial for the feasibility of annotation projection. Fortunately, it appears that at least for some levels of linguistic analysis, a fairly high degree of parallelism can be achieved, provided that the description is situated at an appropriate level of granularity. 2. Instance-level Parallelism While concept-level parallelism is an important property of linguistic theories, it is insufficient for any investigations which are concerned with actual multilingual data such as parallel corpora: Concept-level parallelism is only concerned with the inventory of categories, not with the analysis of concrete utterances.
This makes their integration into more general statistical processing frameworks problematic, since this usually means expressing the output as the optimal solution to a joint optimisation problem. Statistical alignment models, on the other hand, do have an optimisation criterion, but require a large bilingual corpus for training, and external linguistic information is difficult to integrate. Both model families share the problem of choosing from a wealth of possible instantiations. For the purpose of this thesis, we chose to use a statistical word alignment model.