Recognizing Named Entities in Tweets

By Xiaohua Liu, Shaodian Zhang, Furu Wei, M. Zhou. Annual Meeting of the Association for Computational Linguistics, 2011

479 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b52fe0b796e4c899624ed3e9d9ea566453156844

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Citations in the corpus

Tweet Analysis for Real-Time Event Detection and Earthquake Reporting System Development (2013, background): “Some researchers describe their efforts to extract place names from tweets as a part of Named Entity Recognition [15], [16].”
TwiNER: named entity recognition in targeted twitter stream (2012, methodology): “al [9] applied a KNN-based classifier to conduct word-level classification, leveraging the similar and recently labeled tweets.”
Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings (2015, background): “, 2012a), with recent efforts turning towards social media (Finin et al., 2010; Liu et al., 2011; Ritter et al., 2011; Fromreide et al., 2014; Li et al., 2012; Liu et al., 2012).”
Adding semantics to microblog posts (2012, methodology): “Liu et al. [20] focus on NER on tweets and use a semi-supervised learning framework to identify four types of entities.”
Adaptive Co-attention Network for Named Entity Recognition in Tweets (2018, methodology): “Liu et al.(2011) combined a K-Nearest Neighbors (KNN) classifier with a linear Conditional Random Fields (CRF) model under a semisupervised learning.”
What to do about bad language on the internet (2013, background): “…of “NLP for Twitter,” including part-of-speech tagging (Gimpel et al., 2011; Owoputi et al., 2013), named entity recognition (Finin et al., 2010; Ritter et al., 2011; Liu et al., 2011b), parsing (Foster et al., 2011), dialogue modeling (Ritter et al., 2010) and summarization (Shariﬁ et al., 2010).”
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].”
Emoticon Smoothed Language Models for Twitter Sentiment Analysis (2012, background): “Because almost all tweets are public, these rich data offer new opportunities for doing research on data mining and natural language processing(Liu et al. 2011a; 2011b; 2011c; Jiang et al. 2011).”
Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters (2013, background): “…preliminary work on social media part-of-speech (POS) tagging (Gimpel et al., 2011), named entity recognition (Ritter et al., 2011; Liu et al., 2011), and parsing (Foster et al., 2011), but accuracy rates are still significantly lower than traditional well-edited genres like…”
Understandable Big Data: A survey (2015, background): “To improve NER, gazetteers [52] or entities repositories like Freebase [45] have to be extended (many new entities are missing there e.”
Results of the WNUT2017 Shared Task on Novel and Emerging Entity Recognition (2017, background): “Much prior work has avoided assigning tags to these (Ritter et al., 2011; Liu et al., 2011) though accounts often represent not only a person, also organizations, regions, buildings and so on.”
Processing Social Media Messages in Mass Emergency (2014, background): “The most common information extraction task is named entity extraction, which consists of detecting regions of a text referring to people, organizations, or locations [Liu et al. 2011; Ritter et al. 2011].”
Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions (2015, background): “types of data and these types of data also need to be linked with the knowledge base, such as web tables [140], [141], web lists [142], [143], and tweets [144], [145].”
Open domain event extraction from twitter (2012, methodology): “To address Twitter’s noisy style, we follow recent work on NLP in noisy text [46, 31, 19], annotating a corpus of Tweets with events, which is then used as training data for sequence-labeling models to identify event mentions in millions of messages.”
A Survey on Deep Learning for Named Entity Recognition (2018, unknown): “We note that CRF-based NER has been widely applied to texts in various domains, including biomedical text [55], tweets [82], [83] and chemical text [84].”
Named Entity Recognition in Tweets: An Experimental Study (2011, methodology): “Developed in parallel to our work, Liu et al. (2011) investigate NER on the same 3 types, in addition to PRODUCTs and present a semi- supervised approach using k-nearest neighbor.”
Fine-grained location extraction from tweets with temporal awareness (2014, methodology): “However, significant performance degradation has been reported for NER from tweets [18,23].”