Abstract
We present a new dataset comprising tweets for the novel task of detecting biographically relevant utterances. Biographically relevant utterances are all those utterances that reveal some persistent and non-trivial information about the author of a tweet, e.g. habits, (dis)likes, family status, physical appearance, employment information, health issues etc. Unlike previous research we do not restrict biographical relevance to a small fixed set of pre-defined relations. Next to classification experiments employing state-of-the-art classifiers to establish strong baselines for future work, we carry out a linguistic analysis that compares the predictiveness of various high-level features. We also show that the task is different from established tasks, such as aspectual classification or sentiment analysis.- Anthology ID:
- 2022.coling-1.323
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 3669–3679
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.323
- DOI:
- Cite (ACL):
- Michael Wiegand, Rebecca Wilm, and Katja Markert. 2022. Biographically Relevant Tweets – a New Dataset, Linguistic Analysis and Classification Experiments. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3669–3679, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Biographically Relevant Tweets – a New Dataset, Linguistic Analysis and Classification Experiments (Wiegand et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2022.coling-1.323.pdf