Named Entity Recognition in Tweets: An Experimental Study

By Alan Ritter, Sam Clark, Mausam, Oren Etzioni. Conference on Empirical Methods in Natural Language Processing, 2011

1,429 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/14935c3ffb1cafd53a23d84bec66388a77422435

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Citations in the corpus

Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media (2018, methodology): “To extract entities from the collected tweets, we leverage T-NER, a system developed speciﬁcally to perform the task of Named Entity Recognition on tweets (Ritter et al. 2011).”
Extracting Diurnal Patterns of Real World Activity from Social Media (2013, background): “Shimshoni et al. (2009) and Choi and Varian (2012) looked at trends and seasonality in Google searches across categories and estimated their predictability.”
BERTweet: A pre-trained language model for English Tweets (2020, methodology): “Our BERTweet outperforms its main competitors RoBERTa base and XLM-R base on all experimental datasets (with only one exception that XLM-R base does slightly better than BERTweet on Ritter11-T-POS).”
Fine-Grained Entity Recognition (2012, methodology): “Traditional NER systems (Finkel, Grenager, and Manning 2005) use a sequence model for the whole task, usually a linear-chain Conditional Random Field (CRF) (Lafferty, McCallum, and Pereira 2001).”
Robust Named Entity Recognition with Truecasing Pretraining (2019, background): “While there is little work that targets robustness of general NER models, there has been work on NER in noisy domains like twitter (Ritter et al. 2011), and several Workshops on Noisy User-generated Text (WNUT) (Strauss et al. 2016; Derczynski et al. 2017).”
SemEval-2013 Task 2: Sentiment Analysis in Twitter (2013, methodology): “We ﬁrst extracted named entities using a Twitter-tuned NER system (Ritter et al., 2011) from millions of tweets, which we collected over a one-year period spanning from January 2012 to January 2013; we used the public streaming Twitter API to download tweets.”
Open domain event extraction from twitter (2012, methodology): “While we apply an established approach to sequence-labeling tasks in noisy text [46, 31, 19], this is the first work to extract eventreferring phrases in Twitter.”
Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters (2013, methodology): “Ritter et al. (2011) annotated a corpus of 787 tweets23 with a single annotator, using the PTB 21We retain hashtags since by our guidelines a #-prefixed token is ambiguous between a hashtag and a normal word, e.g. #1 or going #home.”
TwiNER: named entity recognition in targeted twitter stream (2012, methodology): “Moreover, we also compare with a tweet-specific NER system (T-NER)12 proposed in [16] on the two tweet collections.”
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].”
Ultra-Fine Entity Typing (2018, background): “, 2014), and precision can suffer when the selected types do not fit the context (Ritter et al., 2011).”
Adaptive Co-attention Network for Named Entity Recognition in Tweets (2018, methodology): “Some Twitter NER datasets have applied 10 top-level Freebase categories (Ritter et al. 2011; Strauss et al. 2016) or have included product classes in addition to PLO (Person, Location, Organization) (Liu et al.”
Analysis of named entity recognition and linking for tweets (2014, methodology): “ystems that will be evaluated in Section 3. 2.2. Comparative Evaluation of Twitter NER We evaluated the tools described above on three available datasets. The rst is the corpus of tweets developed by [12]. This corpus consists of 2,400 tweets (comprising 34K tokens) and was randomly sampled. Tweet I”
Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings (2015, background): “…informal domains (Collins and Singer, 1999; McCallum and Li, 2003; Nadeau and Sekine, 2007; Jin and Chen, 2008; He et al., 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).”
What to do about bad language on the internet (2013, methodology): “…mold 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.,…”