A Tweet Dataset Annotated for Named Entity Recognition and Stance Detection
This provides a resource for researchers working on NLP in Turkish social media, but it is incremental as it extends an existing dataset with new annotations.
The authors created a Turkish tweet dataset annotated for named entity recognition and stance detection, making both annotations publicly available to explore potential relationships between these tasks.
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.