.png){kind=icon}
返回
ProZe: Explainable and Prompt-Guided Zero-Shot Text Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/MIC.2022.3187080.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
As technology accelerates the generation and communication of textual data, the need to automatically understand this content becomes a necessity. In order to classify text, being it for tagging, indexing, or curating documents, one often relies on large, opaque models that are trained on preannotated datasets, making the process unexplainable, difficult to scale, and ill-adapted for niche domains with scarce data. To tackle these challenges, we propose ProZe, a text classification approach that leverages knowledge from two sources: prompting pretrained language models, as well as querying ConceptNet, a common-sense knowledge base which can be used to add a layer of explainability to the results. We evaluate our approach empirically and we show how this combination not only performs on par with state-of-the-art zero shot classification on several domains, but also offers explainable predictions that can be visualized.
Keyword:
Task analysis
Predictive models
Internet
Computational modeling
Semantics
Adaptation models
Transformers
Text classification
Knowledge graphs
zero-shot
explainability
common sense knowledge graph
prompting language models
期刊
IF:
4.4
论文数:
2.0K
被引数:
2.0K
机构
引用论文
.png){kind=icon}
Coordination complexes of niobium and tantalum. V. Eight-coordinated di- and triperoxoniobates(V) and -tantalates(V) with some nitrogen and oxygen bidentate ligands铌和钽的配合物。五。具有一些氮和氧双齿配体的八配位的二过氧化物和三过氧化物铌酸盐 (V) 和钽酸盐 (V)
PREAI
分享收藏
Zero-Shot Text Classification via Knowledge Graph Embedding for Social Media Data基于知识图谱嵌入的社交媒体数据零镜头文本分类
PREAI
分享收藏
查看更多内容