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SCL-SKG: software knowledge extraction with span-level contrastive learning.
- Source :
- Artificial Intelligence Review; 2023 Suppl2, Vol. 56, p2383-2406, 24p
- Publication Year :
- 2023
-
Abstract
- The text of software knowledge community contains abundant knowledge of software engineering field. The software knowledge entity and relation can be extracted automatically and efficiently to form the software knowledge graph, which is helpful for software knowledge-centric intelligent applications, such as intelligent question answering, automatic document generation and software expert recommendation. Most existing methods are confronted with problems of task dependence and entity overlap. In this paper, we propose a software knowledge extraction method based on span-level contrastive learning. From the level of sentence sequence modelling, we model the sentence sequence with span as a unit, and generate abundant positive and negative samples of entity span through the span representation layer to avoid the problem that the token-level method cannot select overlapping entities. From the level of feature learning, we propose supervised entity contrastive learning and relation contrastive learning, which obtain enhanced feature representation of entity span and entity pair through positive and negative sample enhancement and contrastive loss function construction. Experiments are conducted on the dataset which is constructed based on texts of the StackOverflow, and show that our approach achieves a better performance than baseline models. [ABSTRACT FROM AUTHOR]
- Subjects :
- KNOWLEDGE graphs
COMPUTER software
SOFTWARE engineering
QUESTION answering systems
Subjects
Details
- Language :
- English
- ISSN :
- 02692821
- Volume :
- 56
- Database :
- Complementary Index
- Journal :
- Artificial Intelligence Review
- Publication Type :
- Academic Journal
- Accession number :
- 173585816
- Full Text :
- https://doi.org/10.1007/s10462-023-10580-7