Updated README to link an updated dataset

README.md

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 We introduce FiReCS, the first sentiment-annotated corpus of product and service reviews involving Filipino-English code-switching. The data set is composed of 10,487 reviews with a fairly balanced number per sentiment class. Inter-annotator agreement is high with a Kripendorffs’s α for ordinal metric of 0.83. Three human annotators were tasked to manually label reviews according to three polarity classes: Positive, Neutral, and Negative. 
 
+[UPDATE 2024/05/09] An updated dataset containing four polarity classes can be found at [SentiTaglish: Products and Services](https://huggingface.co/datasets/ccosme/SentiTaglishProductsAndServices).
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 ### Supported Tasks and Leaderboards
 
 Sentiment analysis of bilingual text with code-switching / code-mixing.
@@ -65,4 +67,4 @@ The FiReCS data set version 1.0 is released under the [CC-BY-4.0](https://creati
 
 ### Citation Information
 
-TBA
+Cosme, C.J., De Leon, M.M. (2024). Sentiment Analysis of Code-Switched Filipino-English Product and Service Reviews Using Transformers-Based Large Language Models. In: Iglesias, A., Shin, J., Patel, B., Joshi, A. (eds) Proceedings of World Conference on Information Systems for Business Management. ISBM 2023. Lecture Notes in Networks and Systems, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-99-8349-0_11