CubeRE is a model used in natural language processing that helps to predict the relationships between entities in a sentence. It first analyzes the sentence using a language model encoder to understand the context of the words. Then, it creates representations of all possible pairs of entities that may be related in the sentence. These representations help to predict the entity-relation label scores.
How Does CubeRE Work?
In order to understand how CubeRE works, it is important to first under
Overview of HEGCN
HEGCN, also known as Hierarchical Entity Graph Convolutional Network, is a machine learning model used for multi-hop relation extraction across documents. This model is built using a combination of bi-directional long short-term memory (BiLSTM) and graph convolutional networks (GCN) to capture relationships between different elements within documents.
How HEGCN Works
HEGCN utilizes a hierarchical approach to extract relations between different entities within documents. In
Partition Filter Network: An Overview
The Partition Filter Network (PFN) is a valuable framework that has been developed for joint entity and relation extraction. This framework consists of three main components, which are the partition filter encoder, NER unit, and RE unit. With the help of these components, the PFN can perform word pair predictions and provide valuable information related to NER and RE. In this article, we will be taking a closer look at the ins and outs of the Partition Filt