Update README.md

README.md

@@ -146,6 +146,33 @@ This way, the model learns an inner representation of 100 languages that can the
 
 This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.  
 
+
+## Bibtex reference
+
+```text
+
+@inproceedings{schelbECCEEntitycentricCorpus2022,
+	address = {Virtual Event, Lyon France},
+	title = {{ECCE}: {Entity}-centric {Corpus} {Exploration} {Using} {Contextual} {Implicit} {Networks}},
+	copyright = {All rights reserved},
+	isbn = {978-1-4503-9130-6},
+	shorttitle = {{ECCE}},
+	url = {https://dl.acm.org/doi/10.1145/3487553.3524237},
+	doi = {10.1145/3487553.3524237},
+	language = {en},
+	urldate = {2023-02-14},
+	booktitle = {Companion {Proceedings} of the {Web} {Conference} 2022},
+	publisher = {ACM},
+	author = {Schelb, Julian and Ehrmann, Maud and Romanello, Matteo and Spitz, Andreas},
+	month = apr,
+	year = {2022},
+	keywords = {Read},
+	pages = {278--281},
+	file = {Full Text:/Users/julianschelb/Zotero/storage/PECQVBSZ/Schelb et al. - 2022 - ECCE Entity-centric Corpus Exploration Using Cont.pdf:application/pdf},
+}
+
+```
+
 ## Related Papers
 
 * Pan, X., Zhang, B., May, J., Nothman, J., Knight, K., & Ji, H. (2017). Cross-lingual Name Tagging and Linking for 282 Languages. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1946–1958). Association for Computational Linguistics.