Sentiment Inference: Pro and Contra relation dataset

<p>500 German sentences annotated for pro/con relations and polar roles of entities (negative/positive actors/effects): see References for a conceptual introduction.</p> <p>files: annotator1.conll .. annotator3.conll</p> <p>format: conll (parzu parser) with annotations</p> <p>- annotations at the end of the conll parse tree<br> &nbsp; - c = con<br> &nbsp; - p = pro<br> &nbsp; - neff,peff = negative, positive effect<br> &nbsp; - nac, pac = negative, positive actor<br> - the head indices are used for annotation (see below)<br> &nbsp; - c1,6 = Hofstetter con Gewerkschaften<br> &nbsp; - neff6 = negative Effekt on Gewerkschaften</p> <p><br> Note: in these annotations, pro/con is not an intentional relation</p> <p>- in &quot;Snow blocks the driveway&quot; it holds: con(snow,driveway)<br> - &quot;snow&quot; is a negative element wrt. to driveway<br> - use our animacy classifier to identify those case with an actor (see References lrec, available via IGGSA download)</p> <p><br> Example:<br> 1&nbsp;&nbsp; &nbsp;Hofstetter&nbsp;&nbsp; &nbsp;Hofstetter&nbsp;&nbsp; &nbsp;N&nbsp;&nbsp; &nbsp;NE&nbsp;&nbsp; &nbsp;_|Nom|Sg&nbsp;&nbsp; &nbsp;2&nbsp;&nbsp; &nbsp;subj&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;<br> 2&nbsp;&nbsp; &nbsp;wirft&nbsp;&nbsp; &nbsp;werfen&nbsp;&nbsp; &nbsp;V&nbsp;&nbsp; &nbsp;VVFIN&nbsp;&nbsp; &nbsp;3|Sg|Pres|Ind&nbsp;&nbsp; &nbsp;0&nbsp;&nbsp; &nbsp;root&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;<br> 3&nbsp;&nbsp; &nbsp;im&nbsp;&nbsp; &nbsp;in&nbsp;&nbsp; &nbsp;PREP&nbsp;&nbsp; &nbsp;APPRART&nbsp;&nbsp; &nbsp;Dat&nbsp;&nbsp; &nbsp;2&nbsp;&nbsp; &nbsp;pp&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;<br> 4&nbsp;&nbsp; &nbsp;Interview&nbsp;&nbsp; &nbsp;Interview&nbsp;&nbsp; &nbsp;N&nbsp;&nbsp; &nbsp;NN&nbsp;&nbsp; &nbsp;Neut|Dat|Sg&nbsp;&nbsp; &nbsp;3&nbsp;&nbsp; &nbsp;pn&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;<br> 5&nbsp;&nbsp; &nbsp;den&nbsp;&nbsp; &nbsp;die&nbsp;&nbsp; &nbsp;ART&nbsp;&nbsp; &nbsp;ART&nbsp;&nbsp; &nbsp;Def|Fem|Dat|Pl&nbsp;&nbsp; &nbsp;6&nbsp;&nbsp; &nbsp;det&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;<br> 6&nbsp;&nbsp; &nbsp;Gewerkschaften&nbsp;&nbsp; &nbsp;Gewerkschaft&nbsp;&nbsp; &nbsp;N&nbsp;&nbsp; &nbsp;NN&nbsp;&nbsp; &nbsp;Fem|Dat|Pl&nbsp;&nbsp; &nbsp;2&nbsp;&nbsp; &nbsp;objd&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;<br> 7&nbsp;&nbsp; &nbsp;vor&nbsp;&nbsp; &nbsp;vor&nbsp;&nbsp; &nbsp;PTKVZ&nbsp;&nbsp; &nbsp;PTKVZ&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;2&nbsp;&nbsp; &nbsp;avz&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;<br> 8&nbsp;&nbsp; &nbsp;,&nbsp;&nbsp; &nbsp;,&nbsp;&nbsp; &nbsp;$,&nbsp;&nbsp; &nbsp;$,&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;0&nbsp;&nbsp; &nbsp;root&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;<br> 9&nbsp;&nbsp; &nbsp;sie&nbsp;&nbsp; &nbsp;sie&nbsp;&nbsp; &nbsp;PRO&nbsp;&nbsp; &nbsp;PPER&nbsp;&nbsp; &nbsp;3|Pl|_|Nom&nbsp;&nbsp; &nbsp;10&nbsp;&nbsp; &nbsp;subj&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;<br> 10&nbsp;&nbsp; &nbsp;wollen&nbsp;&nbsp; &nbsp;wollen&nbsp;&nbsp; &nbsp;V&nbsp;&nbsp; &nbsp;VMFIN&nbsp;&nbsp; &nbsp;3|Pl|Pres|_&nbsp;&nbsp; &nbsp;2&nbsp;&nbsp; &nbsp;s&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;<br> 11&nbsp;&nbsp; &nbsp;die&nbsp;&nbsp; &nbsp;die&nbsp;&nbsp; &nbsp;ART&nbsp;&nbsp; &nbsp;ART&nbsp;&nbsp; &nbsp;Def|Fem|_|Sg&nbsp;&nbsp; &nbsp;12&nbsp;&nbsp; &nbsp;det&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;<br> 12&nbsp;&nbsp; &nbsp;Branche&nbsp;&nbsp; &nbsp;Branche&nbsp;&nbsp; &nbsp;N&nbsp;&nbsp; &nbsp;NN&nbsp;&nbsp; &nbsp;Fem|_|Sg&nbsp;&nbsp; &nbsp;13&nbsp;&nbsp; &nbsp;obja&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;<br> 13&nbsp;&nbsp; &nbsp;anschw&auml;rzen&nbsp;&nbsp; &nbsp;anschw&auml;rzen&nbsp;&nbsp; &nbsp;V&nbsp;&nbsp; &nbsp;VVINF&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;10&nbsp;&nbsp; &nbsp;aux&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;<br> 14&nbsp;&nbsp; &nbsp;.&nbsp;&nbsp; &nbsp;.&nbsp;&nbsp; &nbsp;$.&nbsp;&nbsp; &nbsp;$.&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;0&nbsp;&nbsp; &nbsp;root&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;_&nbsp;&nbsp; &nbsp;<br> c1,6<br> p1,12<br> neff6</p> <p><br> References:</p> <p>@inproceedings{stance,<br> &nbsp; &nbsp; &nbsp; &nbsp;booktitle = {LSDSem 2017/LSD-Sem Linking Models of Lexical, Sentential and Discourse-level Semantics},<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;month = {April},<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;title = {Stance Detection in Facebook Posts of a German Right-wing Party},<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; author = {Manfred Klenner and Don Tuggener and Simon Clematide},<br> &nbsp; &nbsp; &nbsp; &nbsp;publisher = {ResearchBib},<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; year = {2017},<br> &nbsp; &nbsp; &nbsp; &nbsp; language = {english},<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;url = {https://doi.org/10.5167/uzh-136567}<br> }<br> @inproceedings{perspectives,<br> &nbsp; &nbsp; &nbsp; &nbsp;booktitle = {18th International Conference on Computational Linguistics and Intelligent Text Processing},<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;month = {April},<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;title = {Verb-mediated Composition of Attitude Relations Comprising Reader and Writer Perspective},<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; author = {Manfred Klenner and Simon Clematide and Don Tuggener},<br> &nbsp; &nbsp; &nbsp; &nbsp;publisher = {ResearchBib},<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; year = {2017},<br> &nbsp; &nbsp; &nbsp; &nbsp; language = {english},<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;url = {https://doi.org/10.5167/uzh-136569},<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;doi = {10.1007/978-3-319-77116-8\_11}<br> }<br> @inproceedings{harmonization,<br> &nbsp; &nbsp; &nbsp; &nbsp;booktitle = {Proceedings of the 5th Swiss Text Analytics Conference (SwissText) \&amp; 16th Conference on Natural Language Processing (KONVENS)},<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; editor = {Sarah Ebling and Don Tuggener and Manuela H{\&quot;u}rlimann and Martin Volk},<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;month = {Juni 2020},<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;title = {Harmonization Sometimes Harms},<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; author = {Manfred Klenner and Anne G{\&quot;o}hring and Michael Amsler},<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; publisher = {Virtual Event}<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; year = {2020},<br> &nbsp; &nbsp; &nbsp; &nbsp; language = {english},<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;url = {https://doi.org/10.5167/uzh-197961}<br> }<br> @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>