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Knowledge Extraction in Low-Resource Scenarios: Survey and Perspective
- Publication Year :
- 2022
- Publisher :
- arXiv, 2022.
-
Abstract
- Knowledge Extraction (KE), aiming to extract structural information from unstructured texts, often suffers from data scarcity and emerging unseen types, i.e., low-resource scenarios. Many neural approaches to low-resource KE have been widely investigated and achieved impressive performance. In this paper, we present a literature review towards KE in low-resource scenarios, and systematically categorize existing works into three paradigms: (1) exploiting higher-resource data, (2) exploiting stronger models, and (3) exploiting data and models together. In addition, we highlight promising applications and outline some potential directions for future research. We hope that our survey can help both the academic and industrial communities to better understand this field, inspire more ideas, and boost broader applications.<br />Comment: Work in Progress. Github: \url{https://github.com/zjunlp/Low-resource-KEPapers}
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Computer Science - Computation and Language
Artificial Intelligence (cs.AI)
Computer Science - Artificial Intelligence
Computation and Language (cs.CL)
Information Retrieval (cs.IR)
Computer Science - Information Retrieval
Machine Learning (cs.LG)
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....777685f14eb0f429bb5355bcac531b28
- Full Text :
- https://doi.org/10.48550/arxiv.2202.08063