This is the training data for an animacy classifier (see References LREC) 1) gold_actor: 7468 nouns denoting animate entities 2) gold_nonactor 5511 nouns denoting non-animate entities subsets of 1: gold_direct 6897 nouns directly denoting animate entities gold_metonym 587 metonymy trigger nouns gold_female 3738 nouns denoting female actors gold_male 2830 nouns denoting male actors gold_nogender 329 nouns denoting female or male actors (often plural) Format: just lists Note: although some person names are in the data, a separate NER for person names should be used . References: @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.} }