  Social media data such as Twitter messages ("tweets") pose a particular
challenge to NLP systems because of their short, noisy, and colloquial nature.
Tasks such as Named Entity Recognition (NER) and syntactic parsing require
highly domain-matched training data for good performance. To date, there is no
complete training corpus for both NER and syntactic analysis (e.g., part of
speech tagging, dependency parsing) of tweets. While there are some publicly
available annotated NLP datasets of tweets, they are only designed for
individual tasks. In this study, we aim to create Tweebank-NER, an English NER
corpus based on Tweebank V2 (TB2), train state-of-the-art (SOTA) Tweet NLP
models on TB2, and release an NLP pipeline called Twitter-Stanza. We annotate
named entities in TB2 using Amazon Mechanical Turk and measure the quality of
our annotations. We train the Stanza pipeline on TB2 and compare with
alternative NLP frameworks (e.g., FLAIR, spaCy) and transformer-based models.
The Stanza tokenizer and lemmatizer achieve SOTA performance on TB2, while the
Stanza NER tagger, part-of-speech (POS) tagger, and dependency parser achieve
competitive performance against non-transformer models. The transformer-based
models establish a strong baseline in Tweebank-NER and achieve the new SOTA
performance in POS tagging and dependency parsing on TB2. We release the
dataset 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}.
