Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Conference Paper

Modeling Communicative Purpose with Functional Style: Corpus and Features for German Genre and Register Analysis


Haider,  Thomas
Department of Language and Literature, Max Planck Institute for Empirical Aesthetics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Haider, T., & Palmer, A. (2017). Modeling Communicative Purpose with Functional Style: Corpus and Features for German Genre and Register Analysis. In Proceedings of the Workshop on Stylistic Variation, EMNLP 2017 (pp. 74-84). Copenhagen, Stroudsburg, PA: Association for Computational Linguistics.

Cite as: https://hdl.handle.net/11858/00-001M-0000-002D-E326-1
While there is wide acknowledgement in NLP of the utility of document characterization by genre, it is quite difficult to determine a definitive set of features or even a comprehensive list of genres. This paper addresses both issues. First, with prototype semantics, we develop a hierarchicaltaxonomy of discourse functions. We implement the taxonomy by developing a new text genre corpus of contemporary German to perform a text based comparativeregister analysis. Second, we extract a host of style features, both deep and shallow, aiming beyond linguistically motivated features at situational correlates in texts. The feature sets are used for supervised text genre classification, on which our models achieve high accuracy. The combination of the corpus typology and feature sets allows us to characterize types of communicative purpose in a comparative setup, by qualitative interpretation of style feature loadings of a regularized discriminant analysis. Finally, to determine the dependence of genre on topics (which are arguably the distinguishing factor of

sub-genre), we compare and combine our style models with Latent Dirichlet Allocation features across different corpus settings with unstable topics.