Update README.md

README.md

@@ -51,9 +51,9 @@ print('Prediction:', prediction)
 ```
 @inproceedings{mraikhat-etal-2024-areej,
     title = "{AREE}j: {A}rabic Relation Extraction with Evidence",
-    author = "Mraikhat, Osama  and
+    author = "Rakan Al Mraikhat, Osama  and
       Hamoud, Hadi  and
-      Zaraket, Fadi",
+      Zaraket, Fadi A.",
     editor = "Habash, Nizar  and
       Bouamor, Houda  and
       Eskander, Ramy  and
@@ -76,9 +76,10 @@ print('Prediction:', prediction)
     year = "2024",
     address = "Bangkok, Thailand",
     publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2024.arabicnlp-1.6",
+    url = "https://aclanthology.org/2024.arabicnlp-1.6/",
+    doi = "10.18653/v1/2024.arabicnlp-1.6",
     pages = "67--72",
-    abstract = "Relational entity extraction is key in building knowledge graphs. A relational entity has a source, a tail and atype. In this paper, we consider Arabic text and introduce evidence enrichment which intuitivelyinforms models for better predictions. Relational evidence is an expression in the textthat explains how sources and targets relate. {\%}It also provides hints from which models learn. This paper augments the existing relational extraction dataset with evidence annotation to its 2.9-million Arabic relations.We leverage the augmented dataset to build , a relation extraction with evidence model from Arabic documents. The evidence augmentation model we constructed to complete the dataset achieved .82 F1-score (.93 precision, .73 recall). The target outperformed SOTA mREBEL with .72 F1-score (.78 precision, .66 recall).",
+    abstract = "Relational entity extraction is key in building knowledge graphs. A relational entity has a source, a tail and atype. In this paper, we consider Arabic text and introduce evidence enrichment which intuitivelyinforms models for better predictions. Relational evidence is an expression in the textthat explains how sources and targets relate. {\%}It also provides hints from which models learn. This paper augments the existing relational extraction dataset with evidence annotation to its 2.9-million Arabic relations.We leverage the augmented dataset to build , a relation extraction with evidence model from Arabic documents. The evidence augmentation model we constructed to complete the dataset achieved .82 F1-score (.93 precision, .73 recall). The target outperformed SOTA mREBEL with .72 F1-score (.78 precision, .66 recall)."
 }
 ```
 ### License