# Parsing Tweets into Universal Dependencies

arXiv: https://arxiv.org/abs/1804.08228 · HTML: https://mlsanity.com/paper/1804.08228/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 19 of 19)

- [Multi-Source Domain Adaptation with Mixture of Experts](https://mlsanity.com/paper/1809.02256/) (2018)
- [Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts](https://mlsanity.com/paper/2210.03797/) (2022)
- [IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary Initialization](https://mlsanity.com/paper/2109.04607/) (2021)
- [Subspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training](https://mlsanity.com/paper/2310.16484/) (2023)
- [Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis](https://mlsanity.com/paper/2201.07281/) (2022)
- [Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data](https://mlsanity.com/paper/2403.12749/) (2024)
- [Monotonicity Marking from Universal Dependency Trees](https://mlsanity.com/paper/2104.08659/) (2021)
- [Data-driven Parsing Evaluation for Child-Parent Interactions](https://mlsanity.com/paper/2209.13778/) (2022)
- [Neural Supervised Domain Adaptation by Augmenting Pre-trained Models with Random Units](https://mlsanity.com/paper/2106.04935/) (2021)
- [Out-of-Domain Evaluation of Finnish Dependency Parsing](https://mlsanity.com/paper/2204.10621/) (2022)
- [Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training](https://mlsanity.com/paper/2305.12634/) (2023)
- [OpenNER 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages](https://mlsanity.com/paper/2412.09587/) (2024)
- [Creating a Universal Dependencies Treebank of Spoken Frisian-Dutch Code-switched Data](https://mlsanity.com/paper/2102.11152/) (2021)
- [Cross-Register Projection for Headline Part of Speech Tagging](https://mlsanity.com/paper/2109.07483/) (2021)
- [Technical Report: Impact of Position Bias on Language Models in Token Classification](https://mlsanity.com/paper/2304.13567/) (2023)
- [MultiLexNorm++: A Unified Benchmark and a Generative Model for Lexical Normalization for Asian Languages](https://mlsanity.com/paper/2601.16623/) (2026)
- [TransPOS: Transformers for Consolidating Different POS Tagset Datasets](https://mlsanity.com/paper/2209.11959/) (2022)
- [Weakly Supervised Headline Dependency Parsing](https://mlsanity.com/paper/2301.10371/) (2023)
- [Unsupervised Domain Adaptation using Lexical Transformations and Label Injection for Twitter Data](https://mlsanity.com/paper/2307.10210/) (2023)

## Abstract (arXiv)

We study the problem of analyzing tweets with Universal Dependencies. We extend the UD guidelines to cover special constructions in tweets that affect tokenization, part-of-speech tagging, and labeled dependencies. Using the extended guidelines, we create a new tweet treebank for English (Tweebank v2) that is four times larger than the (unlabeled) Tweebank v1 introduced by Kong et al. (2014). We characterize the disagreements between our annotators and show that it is challenging to deliver consistent annotation due to ambiguity in understanding and explaining tweets. Nonetheless, using the new treebank, we build a pipeline system to parse raw tweets into UD. To overcome annotation noise without sacrificing computational efficiency, we propose a new method to distill an ensemble of 20 transition-based parsers into a single one. Our parser achieves an improvement of 2.2 in LAS over the un-ensembled baseline and outperforms parsers that are state-of-the-art on other treebanks in both accuracy and speed.