Update README.md

Added citation information

README.md

@@ -1,72 +1,87 @@
----
-language:
-- it
-license: cc-by-nc-sa-4.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
-  - split: test_ood
-    path: data/test_ood-*
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: text
-    dtype: string
-  - name: emotion_labels
-    sequence:
-      class_label:
-        names:
-          '0': Anger
-          '1': Anticipation
-          '2': Disgust
-          '3': Fear
-          '4': Joy
-          '5': Love
-          '6': Neutral
-          '7': Sadness
-          '8': Surprise
-          '9': Trust
-  - name: target_labels
-    sequence:
-      class_label:
-        names:
-          '0': Direction
-          '1': Topic
-  splits:
-  - name: train
-    num_bytes: 1010988
-    num_examples: 5966
-  - name: test
-    num_bytes: 169792
-    num_examples: 1000
-  - name: test_ood
-    num_bytes: 137719
-    num_examples: 1000
-  download_size: 844581
-  dataset_size: 1318499
----
-
-### EMit
-**Disclaimer: This dataset is not the official EMit repository from EVALITA. For the official repository and more information, please visit the [EVALITA EMit page](http://www.di.unito.it/~tutreeb/emit23/index.html) or the [EMit repository](https://github.com/oaraque/emit).**
-
-#### Overview
-
-The EMit dataset is a comprehensive resource for the detection of emotions in Italian social media texts. This dataset was created for the EMit shared task, organized as part of Evalita 2023. The EMit dataset consists of social media messages about TV shows, TV series, music videos, and advertisements. Each message is annotated with one or more of the 8 primary emotions defined by Plutchik (anger, anticipation, disgust, fear, joy, sadness, surprise, trust), as well as an additional label “love.” 
-
-#### Annotations
-
-The dataset includes multilabel annotations for each text, indicating the presence of specific emotions. An additional auxiliary subtask involves identifying the target of the affective comments, whether they are directed at the topic of the content or at issues under the control of the direction (e.g., production quality or artistic direction).
-
-#### Structure
-
-The dataset is composed of the following fields:
-- `id`: Identifier for the entry.
-- `text`: Social media messages related to TV shows, TV series, music videos, and advertisements.
-- `emotion_labels`: Anger, anticipation, disgust, fear, joy, sadness, surprise, trust, and love.
-- `target_labels`: Topic, direction, both, or neither.
-
+---
+language:
+- it
+license: cc-by-nc-sa-4.0
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
+  - split: test_ood
+    path: data/test_ood-*
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: text
+    dtype: string
+  - name: emotion_labels
+    sequence:
+      class_label:
+        names:
+          '0': Anger
+          '1': Anticipation
+          '2': Disgust
+          '3': Fear
+          '4': Joy
+          '5': Love
+          '6': Neutral
+          '7': Sadness
+          '8': Surprise
+          '9': Trust
+  - name: target_labels
+    sequence:
+      class_label:
+        names:
+          '0': Direction
+          '1': Topic
+  splits:
+  - name: train
+    num_bytes: 1010988
+    num_examples: 5966
+  - name: test
+    num_bytes: 169792
+    num_examples: 1000
+  - name: test_ood
+    num_bytes: 137719
+    num_examples: 1000
+  download_size: 844581
+  dataset_size: 1318499
+---
+
+### EMit
+**Disclaimer: This dataset is not the official EMit repository from EVALITA. For the official repository and more information, please visit the [EVALITA EMit page](http://www.di.unito.it/~tutreeb/emit23/index.html) or the [EMit repository](https://github.com/oaraque/emit).**
+
+#### Overview
+
+The EMit dataset is a comprehensive resource for the detection of emotions in Italian social media texts. This dataset was created for the EMit shared task, organized as part of Evalita 2023. The EMit dataset consists of social media messages about TV shows, TV series, music videos, and advertisements. Each message is annotated with one or more of the 8 primary emotions defined by Plutchik (anger, anticipation, disgust, fear, joy, sadness, surprise, trust), as well as an additional label “love.” 
+
+#### Annotations
+
+The dataset includes multilabel annotations for each text, indicating the presence of specific emotions. An additional auxiliary subtask involves identifying the target of the affective comments, whether they are directed at the topic of the content or at issues under the control of the direction (e.g., production quality or artistic direction).
+
+#### Structure
+
+The dataset is composed of the following fields:
+- `id`: Identifier for the entry.
+- `text`: Social media messages related to TV shows, TV series, music videos, and advertisements.
+- `emotion_labels`: Anger, anticipation, disgust, fear, joy, sadness, surprise, trust, and love.
+- `target_labels`: Topic, direction, both, or neither.
+
+
+#### Citation
+
+If you use this dataset, please cite the original authors:
+```
+@inproceedings{araque2023emit,
+        title={EMit at EVALITA 2023: Overview of the Categorical Emotion Detection in Italian Social Media Task},
+        author={Araque, O and Frenda, S and Sprugnoli, R and Nozza, D and Patti, V and others},
+        booktitle={CEUR WORKSHOP PROCEEDINGS},
+        volume={3473},
+        pages={1--8},
+        year={2023},
+        organization={CEUR-WS}
+        }
+```