animacy data for animcay classification

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