Update citation info

README.md

@@ -83,18 +83,20 @@ Further non-name entities:
 |`AGE` |Age
 |`ID`| Identifier
 
-### If you se this model, please cite:
+### If you use this model, please cite:
 
 ```bibtex
-@InProceedings{novak-novak:2022:LREC,
-  author    = {Nov{\'{a}}k, Attila  and  Nov{\'{a}}k, Borb{\'{a}}la},
-  title     = {NerKor+Cars-OntoNotes++},
-  booktitle      = {Proceedings of the 13th Language Resources and Evaluation Conference (LREC 2022)},
-  month          = {June},
-  year           = {2022},
-  address        = {Marseille, France},
-  publisher      = {European Language Resources Association},
-  pages     = {1907--1916},
-    url       = {http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.203.pdf}
+@inproceedings{novak-novak-2022-nerkor,
+    title = "{N}er{K}or+{C}ars-{O}nto{N}otes++",
+    author = "Nov{\'a}k, Attila  and
+      Nov{\'a}k, Borb{\'a}la",
+    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
+    month = jun,
+    year = "2022",
+    address = "Marseille, France",
+    publisher = "European Language Resources Association",
+    url = "https://aclanthology.org/2022.lrec-1.203",
+    pages = "1907--1916",
+    abstract = "In this paper, we present an upgraded version of the Hungarian NYTK-NerKor named entity corpus, which contains about twice as many annotated spans and 7 times as many distinct entity types as the original version. We used an extended version of the OntoNotes 5 annotation scheme including time and numerical expressions. NerKor is the newest and biggest NER corpus for Hungarian containing diverse domains. We applied cross-lingual transfer of NER models trained for other languages based on multilingual contextual language models to preannotate the corpus. We corrected the annotation semi-automatically and manually. Zero-shot preannotation was very effective with about 0.82 F1 score for the best model. We also added a 12000-token subcorpus on cars and other motor vehicles. We trained and release a transformer-based NER tagger for Hungarian using the annotation in the new corpus version, which provides similar performance to an identical model trained on the original version of the corpus.",
 }
 ```