Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 377-400 |
| Seitenumfang | 24 |
| Fachzeitschrift | International Journal on Digital Libraries |
| Jahrgang | 25 |
| Ausgabenummer | 2 |
| Frühes Online-Datum | 22 Jan. 2024 |
| Publikationsstatus | Veröffentlicht - Juni 2024 |
Abstract
The automatic semantic structuring of scientific text allows for more efficient reading of research articles and is an important indexing step for academic search engines. Sequential sentence classification is an essential structuring task and targets the categorisation of sentences based on their content and context. However, the potential of transfer learning for sentence classification across different scientific domains and text types, such as full papers and abstracts, has not yet been explored in prior work. In this paper, we present a systematic analysis of transfer learning for scientific sequential sentence classification. For this purpose, we derive seven research questions and present several contributions to address them: (1) We suggest a novel uniform deep learning architecture and multi-task learning for cross-domain sequential sentence classification in scientific text. (2) We tailor two transfer learning methods to deal with the given task, namely sequential transfer learning and multi-task learning. (3) We compare the results of the two best models using qualitative examples in a case study. (4) We provide an approach for the semi-automatic identification of semantically related classes across annotation schemes and analyse the results for four annotation schemes. The clusters and underlying semantic vectors are validated using k-means clustering. (5) Our comprehensive experimental results indicate that when using the proposed multi-task learning architecture, models trained on datasets from different scientific domains benefit from one another. Our approach significantly outperforms state of the art on full paper datasets while being on par for datasets consisting of abstracts.
ASJC Scopus Sachgebiete
- Sozialwissenschaften (insg.)
- Bibliotheks- und Informationswissenschaften
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in: International Journal on Digital Libraries, Jahrgang 25, Nr. 2, 06.2024, S. 377-400.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Sequential sentence classification in research papers using cross-domain multi-task learning
AU - Brack, Arthur
AU - Entrup, Elias
AU - Stamatakis, Markos
AU - Buschermöhle, Pascal
AU - Hoppe, Anett
AU - Ewerth, Ralph
N1 - Publisher Copyright: © The Author(s) 2024.
PY - 2024/6
Y1 - 2024/6
N2 - The automatic semantic structuring of scientific text allows for more efficient reading of research articles and is an important indexing step for academic search engines. Sequential sentence classification is an essential structuring task and targets the categorisation of sentences based on their content and context. However, the potential of transfer learning for sentence classification across different scientific domains and text types, such as full papers and abstracts, has not yet been explored in prior work. In this paper, we present a systematic analysis of transfer learning for scientific sequential sentence classification. For this purpose, we derive seven research questions and present several contributions to address them: (1) We suggest a novel uniform deep learning architecture and multi-task learning for cross-domain sequential sentence classification in scientific text. (2) We tailor two transfer learning methods to deal with the given task, namely sequential transfer learning and multi-task learning. (3) We compare the results of the two best models using qualitative examples in a case study. (4) We provide an approach for the semi-automatic identification of semantically related classes across annotation schemes and analyse the results for four annotation schemes. The clusters and underlying semantic vectors are validated using k-means clustering. (5) Our comprehensive experimental results indicate that when using the proposed multi-task learning architecture, models trained on datasets from different scientific domains benefit from one another. Our approach significantly outperforms state of the art on full paper datasets while being on par for datasets consisting of abstracts.
AB - The automatic semantic structuring of scientific text allows for more efficient reading of research articles and is an important indexing step for academic search engines. Sequential sentence classification is an essential structuring task and targets the categorisation of sentences based on their content and context. However, the potential of transfer learning for sentence classification across different scientific domains and text types, such as full papers and abstracts, has not yet been explored in prior work. In this paper, we present a systematic analysis of transfer learning for scientific sequential sentence classification. For this purpose, we derive seven research questions and present several contributions to address them: (1) We suggest a novel uniform deep learning architecture and multi-task learning for cross-domain sequential sentence classification in scientific text. (2) We tailor two transfer learning methods to deal with the given task, namely sequential transfer learning and multi-task learning. (3) We compare the results of the two best models using qualitative examples in a case study. (4) We provide an approach for the semi-automatic identification of semantically related classes across annotation schemes and analyse the results for four annotation schemes. The clusters and underlying semantic vectors are validated using k-means clustering. (5) Our comprehensive experimental results indicate that when using the proposed multi-task learning architecture, models trained on datasets from different scientific domains benefit from one another. Our approach significantly outperforms state of the art on full paper datasets while being on par for datasets consisting of abstracts.
KW - Multi-task learning
KW - Scholarly communication
KW - Sequential sentence classification
KW - Transfer learning
KW - Zone identification
UR - http://www.scopus.com/inward/record.url?scp=85182839941&partnerID=8YFLogxK
U2 - 10.1007/s00799-023-00392-z
DO - 10.1007/s00799-023-00392-z
M3 - Article
AN - SCOPUS:85182839941
VL - 25
SP - 377
EP - 400
JO - International Journal on Digital Libraries
JF - International Journal on Digital Libraries
SN - 1432-5012
IS - 2
ER -