TweetNERD - End to End Entity Linking Benchmark for Tweets | Neuips 2022 |
Neurips 2022 - Thu 1 Dec 4 p.m. CST — 6 p.m. CST Hall J #1013 https://neurips.cc/virtual/2022/poste...
Mishra, Shubhanshu, Aman, Saini, Raheleh, Makki, Sneha, Mehta, Aria, Haghighi, and Ali, Mollahosseini. "TweetNERD - End to End Entity Linking Benchmark for Tweets.". In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks. 2022.
ArXiv: https://arxiv.org/abs/2210.08129
Dataset: https://doi.org/10.5281/zenodo.6617192
Code: https://github.com/twitter-research/T...
Wikidata page: https://www.wikidata.org/wiki/Q114825474
Abstract: Named Entity Recognition and Disambiguation (NERD) systems are foundational for information retrieval, question answering, event detection, and other natural language processing (NLP) applications. We introduce TweetNERD, a dataset of 340K+ Tweets across 2010-2021, for benchmarking NERD systems on Tweets. This is the largest and most temporally diverse open sourced dataset benchmark for NERD on Tweets and can be used to facilitate research in this area. We describe evaluation setup with TweetNERD for three NERD tasks: Named Entity Recognition (NER), Entity Linking with True Spans (EL), and End to End Entity Linking (End2End); and provide performance of existing publicly available methods on specific TweetNERD splits. TweetNERD is available at: https://zenodo.org/record/6617192 under Creative Commons Attribution 4.0 International (CC BY 4.0) license. Check out more details at https://github.com/twitter-research/T....