500 German sentences annotated for pro/con relations and polar roles of entities (negative/positive actors/effects): see References for a conceptual introduction. files: annotator1.conll .. annotator3.conll format: conll (parzu parser) with annotations - annotations at the end of the conll parse tree - c = con - p = pro - neff,peff = negative, positive effect - nac, pac = negative, positive actor - the head indices are used for annotation (see below) - c1,6 = Hofstetter con Gewerkschaften - neff6 = negative Effekt on Gewerkschaften Note: in these annotations, pro/con is not an intentional relation - in "Snow blocks the driveway" it holds: con(snow,driveway) - "snow" is a negative element wrt. to driveway - use our animacy classifier to identify those case with an actor (see References lrec, available via IGGSA download) Example: 1 Hofstetter Hofstetter N NE _|Nom|Sg 2 subj _ _ 2 wirft werfen V VVFIN 3|Sg|Pres|Ind 0 root _ _ 3 im in PREP APPRART Dat 2 pp _ _ 4 Interview Interview N NN Neut|Dat|Sg 3 pn _ _ 5 den die ART ART Def|Fem|Dat|Pl 6 det _ _ 6 Gewerkschaften Gewerkschaft N NN Fem|Dat|Pl 2 objd _ _ 7 vor vor PTKVZ PTKVZ _ 2 avz _ _ 8 , , $, $, _ 0 root _ _ 9 sie sie PRO PPER 3|Pl|_|Nom 10 subj _ _ 10 wollen wollen V VMFIN 3|Pl|Pres|_ 2 s _ _ 11 die die ART ART Def|Fem|_|Sg 12 det _ _ 12 Branche Branche N NN Fem|_|Sg 13 obja _ _ 13 anschwärzen anschwärzen V VVINF _ 10 aux _ _ 14 . . $. $. _ 0 root _ _ c1,6 p1,12 neff6 References: @inproceedings{stance, booktitle = {LSDSem 2017/LSD-Sem Linking Models of Lexical, Sentential and Discourse-level Semantics}, month = {April}, title = {Stance Detection in Facebook Posts of a German Right-wing Party}, author = {Manfred Klenner and Don Tuggener and Simon Clematide}, publisher = {ResearchBib}, year = {2017}, language = {english}, url = {https://doi.org/10.5167/uzh-136567} } @inproceedings{perspectives, booktitle = {18th International Conference on Computational Linguistics and Intelligent Text Processing}, month = {April}, title = {Verb-mediated Composition of Attitude Relations Comprising Reader and Writer Perspective}, author = {Manfred Klenner and Simon Clematide and Don Tuggener}, publisher = {ResearchBib}, year = {2017}, language = {english}, url = {https://doi.org/10.5167/uzh-136569}, doi = {10.1007/978-3-319-77116-8\_11} } @inproceedings{harmonization, booktitle = {Proceedings of the 5th Swiss Text Analytics Conference (SwissText) \& 16th Conference on Natural Language Processing (KONVENS)}, editor = {Sarah Ebling and Don Tuggener and Manuela H{\"u}rlimann and Martin Volk}, month = {Juni 2020}, title = {Harmonization Sometimes Harms}, author = {Manfred Klenner and Anne G{\"o}hring and Michael Amsler}, publisher = {Virtual Event} year = {2020}, language = {english}, url = {https://doi.org/10.5167/uzh-197961} } @inproceedings{lrec, month = {Juni}, author = {Manfred Klenner and Anne G{\"o}hring}, booktitle = {Proceedings of the Language Resources and Evaluation Conference}, address = {Marseille, France}, title = {Animacy Denoting {G}erman Nouns: Annotation and Classification}, publisher = {European Language Resources Association}, pages = {1360--1364}, year = {2022}, language = {english}, url = {https://doi.org/10.5167/uzh-219148}, abstract = {In this paper, we introduce a gold standard for animacy detection comprising almost 14,500 German nouns that might be used to denote either animate entities or non-animate entities. We present inter-annotator agreement of our crowd-sourced seed annotations (9,000 nouns) and discuss the results of machine learning models applied to this data.} }