A Recursive Annotation Scheme for Referential Information Status. - Arndt Riester; David Lorenz; Nina Seemann (LREC, 2010)

Preservation of Recognizability for Weighted Linear Extended Top-Down Tree Transducers. - Nina Seemann; Daniel Quernheim; Fabienne Braune; Andreas Maletti (ATANLP@EACL, 2012)

Shallow Local Multi-Bottom-up Tree Transducers in Statistical Machine Translation. - Fabienne Braune; Nina Seemann; Daniel Quernheim; Andreas Maletti (ACL (1), 2013)

DIMwid - Decoder Inspection for Moses (using Widgets). - Robin Kurtz; Nina Seemann; Fabienne Braune; Andreas Maletti (Prague Bull. Math. Linguistics, 2013)

String-to-Tree Multi Bottom-up Tree Transducers. - Nina Seemann; Fabienne Braune; Andreas Maletti (ACL (1), 2015)

Rule Selection with Soft Syntactic Features for String-to-Tree Statistical Machine Translation. - Fabienne Braune; Nina Seemann; Alexander M. Fraser (EMNLP, 2015)

A systematic evaluation of MBOT in statistical machine translation. - Nina Seemann; Fabienne Braune; Andreas Maletti (MTSummit, 2015)

Discontinuous Statistical Machine Translation with Target-Side Dependency Syntax. - Nina Seemann; Andreas Maletti (WMT@EMNLP, 2015)

Rule extraction for multi bottom-up tree transducers. - Nina Seemann (2016)

Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German. - Nina Seemann; Marie-Luis Merten; Michaela Geierhos; Doris Tophinke; Eyke Hüllermeier (LaTeCH@ACL, 2017)

Analyzing Constructional Change: Linguistic Annotation and Sources of Uncertainty. - Marie-Luis Merten; Nina Seemann (TEEM, 2018)

UPB-Annotate: Ein maßgeschneidertes Toolkit für historische Texte. - Nina Seemann; Marie-Luis Merten (DHd, 2019)

Effective Detection of Hate Speech Spreaders on Twitter. - Julian Höllig; Yeong Su Lee; Nina Seemann; Michaela Geierhos (CLEF (Working Notes), 2021)

Generalizability of Abusive Language Detection Models on Homogeneous German Datasets. - Nina Seemann; Yeong Su Lee; Julian Höllig; Michaela Geierhos (Datenbank-Spektrum, 2023)

The problem of varying annotations to identify abusive language in social media content. - Nina Seemann; Yeong Su Lee; Julian Höllig; Michaela Geierhos (Nat. Lang. Eng., 2023)