Entity Recognition Models

Partition Filter Network

Introduced by Yan et al. in A Partition Filter Network for Joint Entity and Relation Extraction

Partition Filter Network is a framework designed specifically for joint entity and relation extraction. The framework consists of three components: partition filter encoder, NER unit and RE unit. In task units, we use table-filling for word pair prediction. Orange, yellow and green represents NER-related, shared and RE-related component or features. (b) Detailed depiction of partition filter encoder in one single time step. We decompose feature encoding into two steps: partition and filter (shown in the gray area). In partition, we first segment neurons into two task partitions and one shared partition. Then in filter, partitions are selected and combined to form task-specific features and shared features, filtering out information irrelevant to each task.

Source: A Partition Filter Network for Joint Entity and Relation Extraction

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Joint Entity and Relation Extraction 1 50.00%
Relation Extraction 1 50.00%

Components


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

Categories