Learning to Denoise Distantly-Labeled Data for Entity Typing

Distantly-labeled data can be used to scale up training of statistical models, but it is typically noisy and that noise can vary with the distant labeling technique. In this work, we propose a two-stage procedure for handling this type of data: denoise it with a learned model, then train our final model on clean and denoised distant data with standard supervised training... (read more)

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Datasets


  Add Datasets introduced or used in this paper
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Entity Typing Ontonotes v5 (English) ELMo (distant denoising data) F1 40.2 # 1
Precision 51.5 # 1
Recall 33 # 1

Methods used in the Paper


METHOD TYPE
Sigmoid Activation
Activation Functions
Tanh Activation
Activation Functions
LSTM
Recurrent Neural Networks
BiLSTM
Bidirectional Recurrent Neural Networks
Softmax
Output Functions
ELMo
Word Embeddings