animacy data for animcay classification

This is the training data for an animacy classifier (see References LREC) 1) gold_actor: 7468 nouns denoting animate entities<br> 2) gold_nonactor 5511 nouns denoting non-animate entities subsets of 1: gold_direct 6897 nouns directly denoting animate entities<br> gold_metonym 587 metonymy trigger nouns gold_female 3738 nouns denoting female actors<br> gold_male 2830 nouns denoting male actors<br> gold_nogender 329 nouns denoting female or male actors (often plural) <br> Format: just lists Note: although some person names are in the data, a separate NER for person names should be used . References: @inproceedings{LREC,<br> month = {Juni},<br> author = {Manfred Klenner and Anne G{\"o}hring},<br> booktitle = {Proceedings of the Language Resources and Evaluation Conference},<br> address = {Marseille, France},<br> title = {Animacy Denoting {G}erman Nouns: Annotation and Classification},<br> publisher = {European Language Resources Association},<br> pages = {1360--1364},<br> year = {2022},<br> language = {english},<br> url = {https://doi.org/10.5167/uzh-219148},<br> 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.}<br> }<br>