https://semeval.github.io/system-paper-template.html

## Was ist schon getan?
* intro (halb)
* Data description
* System overview in Stichpunktne
* Linguistic features - teils done

## Was muss noch getan werden?
* letzte Hälfte Intro
* vollständige Tabelle in den Appendix
* System overview von Wiebke durchgesehen werden und dann reinschreiben
* linguistic features ausschreiben
* model selection in experimental setup verschieben?
* experimental setup section schreiben - Notizen dazu in Overleaf
* results: added alternative approaches? Or add section for that
* error analysis: Add ideas/results and write it
    * how often has the classification changed from the roberta to the final classification
* conclusion and limitations: Hanxin/Anna
* abstract
* Anna: analysis nur mit linguistic features ohne Roberta features
%(raus Für Anna: "X_umap_jac_0", "logits_0_roberta", "X_pca_0", "X_umap_0",)
* Systemgrafik erstellen
    * inklusive accuracies für die einzelnen Schritte


% ratio_ADV_word_count
% ratio_PRON_word_count
% ratio_DET_word_count
% ratio_CONJ_word_count
% ratio_cefrj_stem_word_count
% ratio_a2_stem_word_count
% ratio_b1_stem_word_count
% ratio_b2_stem_word_count
% num_freq1_words
% prop_freq_content_words
% prop_unfreq_content_words
% X_umap_0
% NEGATIVE
% POSITIVE
% anger
% fear
% formal
% informal
% joy
% max_depth
% mean_depth
% n_unique_words
% neutral
% ratio_top10_content_words
% X_umap_jac_0
% logits_0_roberta


% Turn into table with label 'features-kept'
 % ratioADVwordcount', 'ratioPRONwordcount', 'ratioDETwordcount', 'ratioCONJwordcount', 'ratiocefrjstemwordcount',
 %       'ratioa2stemwordcount', 'ratiob1stemwordcount',
 %       'ratiob2stemwordcount', 'numfreq1words',
 %       'propfreqcontentwords', 'propunfreqcontentwords', 'Xumap0',
 %       'NEGATIVE', 'POSITIVE', 'anger', 'fear', 'formal', 'informal', 'joy',
 %       'maxdepth', 'meandepth', 'n unique words', 'neutral',
 %       'ratio top10 content words', 'X umap jac 0', 'logits 0 roberta'


% Nele: Intro & data description (Verteilung der Daten zw. human & machine in train, test, dev)
% Anna: error analysis & feature description in methods, github aufräumen

% Error analysis draft:
% Classification report for final submission:
% - relatively uniform across all measures


% Classification report for roberta only: 
% - overfitting for label 0, underfitting for machines
% -  

% --> Adding feature information has helped model generalize 


% Classification for base Roberta??
% - 

% Comparison: 
