Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments

By Kevin Gimpel, Nathan Schneider, Brendan T. O'Connor, Dipanjan Das, Daniel Mills, Jacob Eisenstein, Michael Heilman, Dani Yogatama, Jeffrey Flanigan, Noah A. Smith. 2010

1,121 citations recorded by the source corpus.

We address the problem of part-of-speech tagging for English data from the popular micro-blogging service Twitter. We develop a tagset, annotate data, develop features, and report tagging results nearing 90% accuracy. The data and tools have been made available to the research community with the goal of enabling richer text analysis of Twitter and related social media data sets.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

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Citations in the corpus

Gaussian Error Linear Units (GELUs) (2016, methodology): “To meet this challenge we compare the nonlinearities on POS-annotated tweets (Gimpel et al., 2011; Owoputi et al., 2013) which contain 25 tags.”
Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter (2016, methodology): “Features We use a range of features focusing on both the textual information given in the tweets as well as extra-linguistic information including POS tags obtained using Gimpel et al. (2011) and Spacy 2 .”
Demographic Dialectal Variation in Social Media: A Case Study of African-American English (2016, methodology): “To search for the constructions, we tagged the corpora using the ARK Twitter POS tagger (Gimpel et al., 2011; Owoputi et al., 2013), 9 which Jørgensen et al. (2015) show has similar accuracy rates on both AAE and non-AAE tweets, unlike other POS tag-gers.”
SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval) (2019, background): “…(e.g. “Hiiiii” to “Hi”, partially hidden words (“c00l” to “cool”) converting emojis to text, removing uncommon words, and Twitter speciﬁc to-kenizers (Ark Twokenizer 4 (Gimpel et al., 2011), NLTK TweetTokenizer 5 ) as well as standard to-kenizers (Stanford Core NLP (Manning et al., 2014), Keras 6 .”
TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification (2020, background): “There are also other scenarios to be addressed as well, like sequence tagging (Baldwin et al., 2015; Gimpel et al., 2018), multimodality (Schifanella et al., 2016; Lu et al., 2018), and code-switching tasks (Barman et al., 2014; Vilares et al., 2016).”
TWEETQA: A Social Media Focused Question Answering Dataset (2019, neutral): “…tools for core nat-ural language understanding tasks involving syntactic and semantic analysis have been developed for noisy social media text (Gimpel et al., 2011; Ritter et al., 2011; Kong et al., 2014; Wang et al., 2014), there is little work on question answering or reading comprehension…”
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise (2018, background): “The Twitter Part of Speech dataset [3] contains 1,827 tweets annotated with 25 POS tags.”
That’s So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets (2015, background): “In recent years, language technology researchers have focused on developing genre-specific Twitter partof-speech tagging (Gimpel et al., 2011), named entity recognition (Ritter et al., 2011), summarization (O’Connor et al., 2010b), sentiment analysis (Agarwal et al., 2011), event extraction (Ritter…”
Learning a POS tagger for AAVE-like language (2016, methodology): “on the 1827 tweets from the CMU Twitter Corpus (Gimpel et al., 2011).”
Neural Unsupervised Domain Adaptation in NLP—A Survey (2020, background): “, Twitter (Gimpel et al., 2011; Foster et al., 2011).”
Twitter Sentiment Analysis (2015, methodology): “We ﬁnally chose to use ArkTweetNLP library developed by the team of researchers from Carnegie Mellon University (Gimpel et al., 2011) since it was trained on a Twitter dataset.”
Modelling input texts: from Tree Kernels to Deep Learning (2015, methodology): “For POS tagging we use the CMU tagger [51] trained on Twitter data and an off-the-shelf OpenNLP chunker.”
Positive, Negative, or Neutral: Learning an Expanded Opinion Lexicon from Emoticon-Annotated Tweets (2015, methodology): “We use the TweetNLP library [Gimpel et al., 2011], which provides a tokeniser and a tagger specifically for the language used in Twitter.”
A Continuously Growing Dataset of Sentential Paraphrases (2017, methodology): “We preprocessed the tweets using a tok-enizer 5 (Gimpel et al., 2011) and an in-house sentence splitter.”
BERTweet: A pre-trained language model for English Tweets (2020, neutral): “, 2011), ARK-Twitter4 (Gimpel et al., 2011; Owoputi et al., 2013) and TWEEBANK-V25 (Liu et al.”
Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification (2014, methodology): “A tweet-speciﬁc tokenizer (Gimpel et al., 2011) is employed, and the dependency parsing results are computed by Stanford Parser (Klein and Man-ning, 2003).”