Nina Seemann


Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German
Nina Seemann | Marie-Luis Merten | Michaela Geierhos | Doris Tophinke | Eyke Hüllermeier
Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

In this paper, we present the annotation challenges we have encountered when working on a historical language that was undergoing elaboration processes. We especially focus on syntactic ambiguity and gradience in Middle Low German, which causes uncertainty to some extent. Since current annotation tools consider construction contexts and the dynamics of the grammaticalization only partially, we plan to extend CorA - a web-based annotation tool for historical and other non-standard language data - to capture elaboration phenomena and annotator unsureness. Moreover, we seek to interactively learn morphological as well as syntactic annotations.


Rule Selection with Soft Syntactic Features for String-to-Tree Statistical Machine Translation
Fabienne Braune | Nina Seemann | Alexander Fraser
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

A systematic evaluation of MBOT in statistical machine translation
Nina Seemann | Fabienne Braune | Andreas Maletti
Proceedings of Machine Translation Summit XV: Papers

String-to-Tree Multi Bottom-up Tree Transducers
Nina Seemann | Fabienne Braune | Andreas Maletti
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Discontinuous Statistical Machine Translation with Target-Side Dependency Syntax
Nina Seemann | Andreas Maletti
Proceedings of the Tenth Workshop on Statistical Machine Translation


Shallow Local Multi-Bottom-up Tree Transducers in Statistical Machine Translation
Fabienne Braune | Nina Seemann | Daniel Quernheim | Andreas Maletti
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Preservation of Recognizability for Weighted Linear Extended Top-Down Tree Transducers
Nina Seemann | Daniel Quernheim | Fabienne Braune | Andreas Maletti
Proceedings of the Workshop on Applications of Tree Automata Techniques in Natural Language Processing


A Recursive Annotation Scheme for Referential Information Status
Arndt Riester | David Lorenz | Nina Seemann
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

We provide a robust and detailed annotation scheme for information status, which is easy to use, follows a semantic rather than cognitive motivation, and achieves reasonable inter-annotator scores. Our annotation scheme is based on two main assumptions: firstly, that information status strongly depends on (in)definiteness, and secondly, that it ought to be understood as a property of referents rather than words. Therefore, our scheme banks on overt (in)definiteness marking and provides different categories for each class. Definites are grouped according to the information source by which the referent is identified. A special aspect of the scheme is that non-anaphoric expressions (e.g.\ names) are classified as to whether their referents are likely to be known or unknown to an expected audience. The annotation scheme provides a solution for annotating complex nominal expressions which may recursively contain embedded expressions. In annotating a corpus of German radio news bulletins, a kappa score of .66 for the full scheme was achieved, a core scheme of six top-level categories yields kappa = .78.