Update README.md

README.md

@@ -62,7 +62,7 @@ license: cc-by-4.0
 ## Dataset
 The dataset is made up of consumer reviews written in Polish. Those reviews belong to four domains: hotels, medicine, products, and university. This collection also contains non-opinion informative texts belonging to the same domains (meaning they are mostly neutral). Each sentence, as well as all the reviews as a whole, are annotated with emotions from the Plutchnik's wheel of emotions (joy, trust, anticipation, surprise, fear, sadness, disgust, anger), as well as the perceived sentiment (positive, negative, neutral), with ambivalent sentiment being labeled using both positive and negative labels. The dataset was annotated by six people who did not see each other's decisions. These annotations were aggregated by selecting labels annotated by at least 2 out of 6 people, meaning controversial texts and sentences can be annotated with opposing emotions. While each sentence has its own annotation, they were created in the context of the whole review.
 
-For more information about this dataset, see references [1](#ref-1) and [2](#ref-2).
+For more information about this dataset, see the article in the Citation Information section.
 
 ### Training set
 Training data consists of 776 reviews containing 6393 sentences randomly selected from the whole dataset. The split was done on the level of whole reviews, meaning no reviews are split between sets.
@@ -109,7 +109,41 @@ meaning sentences are labeled as:
 and the review as a whole, starting from "Była to pierwsza wizyta ale moze i ostatnia." and ending at "Nie polecam tego lekarza." is labeled as: surprise, sadness, negative.
 
 
-## References
-<span id="ref-1">1. Koptyra, Bartłomiej, et al. "CLARIN-Emo: Training Emotion Recognition Models Using Human Annotation and ChatGPT." International Conference on Computational Science. Cham: Springer Nature Switzerland, 2023.</span>
+### Licensing Information
+
+[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+
+### Citation Information
+
+```
+@article{kocon2023chatgpt,
+  title={ChatGPT: Jack of all trades, master of none},
+  author={Koco{'n}, Jan and Cichecki, Igor and Kaszyca, Oliwier and Kochanek, Mateusz and Szyd{\l}o, Dominika and Baran, Joanna and Bielaniewicz, Julita and Gruza, Marcin and Janz, Arkadiusz and Kanclerz, Kamil and others},
+  journal={Information Fusion},
+  volume={99},
+  pages={101861},
+  year={2023},
+  publisher={Elsevier}
+}
+```
+
+```
+@inproceedings{koptyra2023clarin,
+  title={Clarin-emo: Training emotion recognition models using human annotation and chatgpt},
+  author={Koptyra, Bart{\l}omiej and Ngo, Anh and Radli{'n}ski, {\L}ukasz and Koco{'n}, Jan},
+  booktitle={International Conference on Computational Science},
+  pages={365--379},
+  year={2023},
+  organization={Springer}
+}
+```
+
+```
+@inproceedings{koptyra2024poleval,
+  title={PolEval 2024 Task 2: Emotion and Sentiment Recognition},
+  author={Koptyra, Bart{\l}omiej and Koco{'n}, Jan},
+  booktitle={Proceedings of the PolEval 2024 Workshop},
+  year={2024},
+  organization={Institute of Computer Science, Polish Academy of Sciences}
+}
 
-<span id="ref-2">2. Kocoń, Jan, et al. "ChatGPT: Jack of all trades, master of none." Information Fusion (2023): 101861.</span>