Learning Structured Representations of Entity Names using Active Learning and Weak Supervision

被引：0

作者：

Qian, Kun ^{[1
]}

Raman, Poornima Chozhiyath ^{[1
]}

Popa, Lucian ^{[1
]}

Li, Yunyao ^{[1
]}

机构：

[1] IBM Res, Cambridge, MA 10598 USA

来源：

PROCEEDINGS OF THE 2020 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP) | 2020年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Structured representations of entity names are useful for many entity-related tasks such as entity normalization and variant generation. Learning the implicit structured representations of entity names without context and external knowledge is particularly challenging. In this paper, we present a novel learning framework that combines active learning and weak supervision to solve this problem. Our experimental evaluation show that this framework enables the learning of high-quality models from merely a dozen or so labeled examples.

引用

页码：6376 / 6383

页数：8

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