Update README.md

README.md

@@ -35,4 +35,23 @@ The model contained in this repository constitutes the fundament of the NER syst
 
 
 # System description paper and citation
-The system description paper will be published at Social Media Mining for Health Application (#SMM4H) held on COLING22 in October 2022. 
+[`The system description paper`](https://aclanthology.org/2022.smm4h-1.8/) was be published at Social Media Mining for Health Application (#SMM4H) held on COLING22 in October 2022. 
+
+```
+@inproceedings{chizhikova-etal-2022-sinai,
+    title = "{SINAI}@{SMM}4{H}{'}22: Transformers for biomedical social media text mining in {S}panish",
+    author = "Chizhikova, Mariia  and
+      L{\'o}pez-{\'U}beda, Pilar  and
+      D{\'\i}az-Galiano, Manuel C.  and
+      Ure{\~n}a-L{\'o}pez, L. Alfonso  and
+      Mart{\'\i}n-Valdivia, M. Teresa",
+    booktitle = "Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task",
+    month = oct,
+    year = "2022",
+    address = "Gyeongju, Republic of Korea",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.smm4h-1.8",
+    pages = "27--30",
+    abstract = "This paper covers participation of the SINAI team in Tasks 5 and 10 of the Social Media Mining for Health ({\#}SSM4H) workshop at COLING-2022. These tasks focus on leveraging Twitter posts written in Spanish for healthcare research. The objective of Task 5 was to classify tweets reporting COVID-19 symptoms, while Task 10 required identifying disease mentions in Twitter posts. The presented systems explore large RoBERTa language models pre-trained on Twitter data in the case of tweet classification task and general-domain data for the disease recognition task. We also present a text pre-processing methodology implemented in both systems and describe an initial weakly-supervised fine-tuning phase alongside with a submission post-processing procedure designed for Task 10. The systems obtained 0.84 F1-score on the Task 5 and 0.77 F1-score on Task 10.",
+}
+```