# A Tweet Dataset Annotated for Named Entity Recognition and Stance Detection

arXiv: https://arxiv.org/abs/1901.04787 · HTML: https://mlsanity.com/paper/1901.04787/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2019.

## Built on by (most cited first, 4 of 4)

- [Opinions are Made to be Changed: Temporally Adaptive Stance Classification](https://mlsanity.com/paper/2108.12476/) (2021)
- [Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data](https://mlsanity.com/paper/2403.12749/) (2024)
- [Resources for Turkish Natural Language Processing: A critical survey](https://mlsanity.com/paper/2204.05042/) (2022)
- [TurkishBERTweet: Fast and Reliable Large Language Model for Social Media Analysis](https://mlsanity.com/paper/2311.18063/) (2023)

## Abstract (arXiv)

Annotated datasets in different domains are critical for many supervised learning-based solutions to related problems and for the evaluation of the proposed solutions. Topics in natural language processing (NLP) similarly require annotated datasets to be used for such purposes. In this paper, we target at two NLP problems, named entity recognition and stance detection, and present the details of a tweet dataset in Turkish annotated for named entity and stance information. Within the course of the current study, both the named entity and stance annotations of the included tweets are made publicly available, although previously the dataset has been publicly shared with stance annotations only. We believe that this dataset will be useful for uncovering the possible relationships between named entity recognition and stance detection in tweets.