Update README.md

README.md

@@ -24,11 +24,9 @@ demonstrated strong performance with F1-scores above 95% across all models. The
 across different text domains, with models fine-tuned on NERsocial showing better transferability compared to those trained 
 on similar datasets like WNUT. This makes NERsocial particularly valuable for developing NER systems that can handle both formal 
 and informal communication in HRI applications.
-
-
-
 ***
 
+### Data Format
 ```
 { 
 'tokens': {"0": ["Poco", "Bueno", "was", "a", "American", "Quarter", "Horse", "stallion", "foaled", "April", "10", ",", "1944", "."], "1": ["Formal", "breeds", "often", "considered", "to", "be", "of", "the", "pit", "bull", "type", "include", "the", "American", "Pit", "Bull", "Terrier", ",", "American", "Staffordshire", "Terrier", ",", "American", "Bully", ",", "and", "Staffordshire", "Bull", "Terrier", "."], ... },
@@ -61,3 +59,19 @@ ids_to_labels = {v: k for k, v in labels_to_ids.items()}
 
 ### Usage and License Notices
 The data is provided under an MIT license, so feel free to use it outside of research purposes.
+
+### Citation
+
+If you use this dataset, please cite as follows:
+
+```
+@misc{atuhurra2024nersocialefficientnamedentity,
+  title={NERsocial: Efficient Named Entity Recognition Dataset Construction for Human-Robot Interaction Utilizing RapidNER}, 
+  author={Jesse Atuhurra and Hidetaka Kamigaito and Hiroki Ouchi and Hiroyuki Shindo and Taro Watanabe},
+  year={2024},
+  eprint={2412.09634},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  url={https://arxiv.org/abs/2412.09634}, 
+}
+```