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Social media data such as Twitter messages ("tweets") pose a particularchallenge to NLP systems because of their short, noisy, and colloquial nature.Tasks such as Named Entity Recognition (NER) and syntactic parsing requirehighly domain-matched training data for good performance. To date, there is nocomplete training corpus for both NER and syntactic analysis (e.g., part ofspeech tagging, dependency parsing) of tweets. While there are some publiclyavailable annotated NLP datasets of tweets, they are only designed forindividual tasks. In this study, we aim to create Tweebank-NER, an English NERcorpus based on Tweebank V2 (TB2), train state-of-the-art (SOTA) Tweet NLPmodels on TB2, and release an NLP pipeline called Twitter-Stanza. We annotatenamed entities in TB2 using Amazon Mechanical Turk and measure the quality ofour annotations. We train the Stanza pipeline on TB2 and compare withalternative NLP frameworks (e.g., FLAIR, spaCy) and transformer-based models.The Stanza tokenizer and lemmatizer achieve SOTA performance on TB2, while theStanza NER tagger, part-of-speech (POS) tagger, and dependency parser achievecompetitive performance against non-transformer models. The transformer-basedmodels establish a strong baseline in Tweebank-NER and achieve the new SOTAperformance in POS tagging and dependency parsing on TB2. We release thedataset and make both the Stanza pipeline and BERTweet-based models available"off-the-shelf" for use in future Tweet NLP research. Our source code, data,and pre-trained models are available at:\url{https://github.com/social-machines/TweebankNLP}.

Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis