Astro-mT5: Entity Extraction from Astrophysics Literature using mT5 Language Model
Abstrak
Scientific research requires reading and extracting relevant information from existing scientific literature in an effective way. To gain insights over a collection of such scientific documents, extraction of entities and recognizing their types is considered to be one of the important tasks. Numerous studies have been conducted in this area of research. In our study, we introduce a framework for entity recognition and identification of NASA astrophysics dataset, which was published as a part of the DEAL SharedTask. We use a pre-trained multilingual model, based on a natural language processing framework for the given sequence labeling tasks. Experiments show that our model, Astro-mT5, out-performs the existing baseline in astrophysics related information extraction.
Penulis (4)
Madhusudan Ghosh
Payel Santra
SK Asif Iqbal
Partha Basuchowdhuri
Akses Cepat
PDF tidak tersedia langsung
Cek di sumber asli →- Tahun Terbit
- 2022
- Bahasa
- en
- Total Sitasi
- 7×
- Sumber Database
- Semantic Scholar
- DOI
- 10.18653/v1/2022.wiesp-1.12
- Akses
- Open Access ✓