More refined superbag: Distantly supervised relation extraction with deep clustering.

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Machine Learning

Background:

• Distant supervision (DS) automates data annotation for relation extraction (RE) using knowledge bases and corpora.
• Current DS methods using attention mechanisms struggle with noisy data and information loss.
• This necessitates improved methods for handling noisy labels and maximizing information from training data.

Purpose of the Study:

• To propose DCSR, a novel distant supervision method that employs deep clustering for refined superbag representations.
• To address the problem of incorrect labels in DS for RE.
• To capture valuable information from both clean and noisy data bags.

Main Methods:

• Utilizes deep clustering to replace selective attention for constructing superbags.
• Captures information from spatially-close bags, including noisy ones, to create robust representations.
• Implements data augmentation on input sentences to mitigate the long-tail problem in RE.

Main Results:

• DCSR effectively improves relation extraction performance.
• The proposed method significantly outperforms state-of-the-art approaches on the NYT2010 and NYT-H datasets.
• Deep clustering enhances superbag representations, leading to more accurate RE.

Conclusions:

• DCSR offers a more effective approach to distant supervision for relation extraction.
• The method successfully addresses challenges posed by noisy labels and data sparsity.
• DCSR demonstrates significant advancements in the field of automated relation extraction.