Interaction-and-Response Network for Distantly Supervised Relation Extraction.

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Machine Learning

Background:

• Distantly supervised relation extraction (DSRE) identifies semantic relations in large text corpora.
• Prior methods using selective attention neglect feature dependencies, hindering performance.
• This limitation necessitates novel approaches for more effective relation extraction.

Purpose of the Study:

• To propose a new framework, the Interaction-and-Response Network (IR-Net), to address limitations in DSRE.
• To adaptively recalibrate features at sentence, bag, and group levels by modeling interdependencies.
• To enhance the learning of salient discriminative features for improved entity relation extraction.

Main Methods:

• Developed the Interaction-and-Response Network (IR-Net) framework.
• Implemented interactive and responsive modules across feature hierarchies.
• Explicitly modeled interdependencies among features at multiple levels.

Main Results:

• IR-Net demonstrated significant performance improvements on benchmark DSRE datasets (NYT-10, NYT-16, Wiki-20m).
• The proposed method outperformed ten state-of-the-art DSRE techniques.
• Modeling feature interdependencies proved crucial for enhancing extraction accuracy.

Conclusions:

• The IR-Net framework effectively captures feature dependencies, leading to superior DSRE performance.
• Explicitly modeling interdependencies is a promising direction for advancing relation extraction.
• IR-Net offers a robust solution for identifying semantic relations in massive text data.