Abstract
We investigate the problem of identifying the major hypothesis that is addressed in a scientific paper. To this end, we present a dataset from the domain of invasion biology that organizes a set of 954 papers into a network of fine-grained domain-specific categories of hypotheses. We carry out experiments on classifying abstracts according to these categories and present a pilot study on annotating hypothesis statements within the text. We find that hypothesis statements in our dataset are complex, varied and more or less explicit, and, importantly, spread over the whole abstract. Experiments with BERT-based classifiers show that these models are able to classify complex hypothesis statements to some extent, without being trained on sentence-level text span annotations.- Anthology ID:
- 2022.wiesp-1.5
- Volume:
- Proceedings of the first Workshop on Information Extraction from Scientific Publications
- Month:
- November
- Year:
- 2022
- Address:
- Online
- Venue:
- WIESP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 32–42
- Language:
- URL:
- https://aclanthology.org/2022.wiesp-1.5
- DOI:
- Cite (ACL):
- Marc Brinner, Tina Heger, and Sina Zarriess. 2022. Linking a Hypothesis Network From the Domain of Invasion Biology to a Corpus of Scientific Abstracts: The INAS Dataset. In Proceedings of the first Workshop on Information Extraction from Scientific Publications, pages 32–42, Online. Association for Computational Linguistics.
- Cite (Informal):
- Linking a Hypothesis Network From the Domain of Invasion Biology to a Corpus of Scientific Abstracts: The INAS Dataset (Brinner et al., WIESP 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.wiesp-1.5.pdf