Silver Standard of quantified positive, negative and neutral German noun phrases

<p>Repository: silver standard quantified (simple) noun phrases</p> <p>43,842 positive (+), negative (-) or neutral (0) NPs,&nbsp;</p> <p>e.g. &quot;ein notorischer Verf&uuml;hrer&nbsp;&nbsp; &nbsp;-5.625&quot; is highly negative (-5.625)</p> <p>see References LREC for a description of the data</p> <p>Format: tsv</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>