TwiNER: named entity recognition in targeted twitter stream

By Chenliang Li, Jianshu Weng, Qi He, Yuxia Yao, Anwitaman Datta, Aixin Sun, Bu-Sung Lee. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2012

297 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Communication, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/5feb6d5f47295009ac8286a1cb2aa065aaf3ea9a

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Citations in the corpus

AIDR: artificial intelligence for disaster response (2014, background): “For instance, performing information extraction on short bursts of text (e.g., on 140-character tweets) is signiﬁcantly more diﬃcult than performing the same task on large documents such as blog posts pr news articles [6].”
Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings (2015, background): “Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 548–554, Lisbon, Portugal, 17-21 September 2015. c©2015 Association for Computational Linguistics.”
Adaptive Co-attention Network for Named Entity Recognition in Tweets (2018, methodology): “Numerous methods have been introduced(Ritter et al. 2011; Liu et al. 2011; Li et al. 2012; de Oliveira et al. 2013; Li et al. 2015) to process the content posted by users on social media sites.”
Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning (2020, background): “For example, NER for tweets [45, 47, 59] can detect events or trends [44, 60].”
Sampling Content from Online Social Networks (2015, background): “Some studies have also used Twitter search to gather tweets containing speciﬁc keywords, for example, tweets related to earthquakes in Japan [Sakaki et al. 2010] and Singapore General Election [Li ACM Transactions on the Web, Vol. 9, No. 3, Article 12, Publication date: June 2015.”
On-the-Job Learning with Bayesian Decision Theory (2015, unknown): “In many situations, one wishes to build a new system — e.g., to do Twitter information extraction [5] to aid in disaster relief efforts or monitor public opinion — but one simply lacks the resources to follow either the pure ML or pure crowdsourcing road.”
Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer (2020, methodology): “To adapt existing NER models to social media, various methods have been proposed to incorporate many tweet-speciﬁc features (Ritter et al., 2011; Li et al., 2012, 2014; Limsopatham and Collier, 2016).”