Learning Structured Representations of Entity Names using ActiveLearning and Weak Supervision
Kun Qian,Poornima Chozhiyath Raman,Yunyao Li,Lucian Popa +3 more
- 01 Nov 2020
- pp 6376-6383
TL;DR: This paper presents a novel learning framework that combines active learning and weak supervision to solve the problem of implicit structured representations of entity names without context and external knowledge.
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Abstract: 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.
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A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios.
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
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•Proceedings Article
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke,Sam Gross,Francisco Massa,Adam Lerer,James Bradbury,Gregory Chanan,Trevor Killeen,Zeming Lin,Natalia Gimelshein,Luca Antiga,Alban Desmaison,Andreas Kopf,Edward Z. Yang,Zachary DeVito,Martin Raison,Alykhan Tejani,Sasank Chilamkurthy,Benoit Steiner,Lu Fang,Junjie Bai,Soumith Chintala +20 more
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Active Learning Literature Survey
Burr Settles
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TL;DR: This report provides a general introduction to active learning and a survey of the literature, including a discussion of the scenarios in which queries can be formulated, and an overview of the query strategy frameworks proposed in the literature to date.
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TL;DR: A new approach based on the skipgram model, where each word is represented as a bag of character n-grams, with words being represented as the sum of these representations, which achieves state-of-the-art performance on word similarity and analogy tasks.