Update README.md

README.md

@@ -25,10 +25,9 @@ model-index:
 ---
 ## This model is for efficiency purposes for better accuracy refer to [en_legal_ner_trf](https://huggingface.co/opennyaiorg/en_legal_ner_trf)
 ---
-# To Update
-
-[AUTHORS] "[PAPER NAME]". [PAPER DETAILS] [PAPER LINK]
+# Paper details
 
+[Named Entity Recognition in Indian court judgments](https://aclanthology.org/2022.nllp-1.15)
 ---
 Indian Legal Named Entity Recognition(NER): Identifying relevant named entities in an Indian legal judgement using legal NER trained on [spacy](https://github.com/explosion/spaCy).
 
@@ -111,5 +110,22 @@ for ent in doc.ents:
 ## Author - Publication
 
 ```
-[CITATION DETAILS]
+@inproceedings{kalamkar-etal-2022-named,
+    title = "Named Entity Recognition in {I}ndian court judgments",
+    author = "Kalamkar, Prathamesh  and
+      Agarwal, Astha  and
+      Tiwari, Aman  and
+      Gupta, Smita  and
+      Karn, Saurabh  and
+      Raghavan, Vivek",
+    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2022",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.nllp-1.15",
+    doi = "10.18653/v1/2022.nllp-1.15",
+    pages = "184--193",
+    abstract = "Identification of named entities from legal texts is an essential building block for developing other legal Artificial Intelligence applications. Named Entities in legal texts are slightly different and more fine-grained than commonly used named entities like Person, Organization, Location etc. In this paper, we introduce a new corpus of 46545 annotated legal named entities mapped to 14 legal entity types. The Baseline model for extracting legal named entities from judgment text is also developed.",
+}
 ```