Joint Extraction of Entity and Relation with Information Redundancy Elimination

TL;DR

This paper introduces a joint extraction model that effectively reduces redundancy and overlaps in entity and relation extraction tasks, utilizing a novel Encoder-LSTM and attention mechanisms.

Contribution

The paper presents a new joint extraction model with Encoder-LSTM and attention mechanisms that directly extracts multiple related entity pairs while minimizing redundant information.

Findings

Achieves high performance on ADE and CoNLL04 datasets.

Significantly reduces redundant information in extraction.

Outperforms existing models in accuracy and efficiency.

Abstract

To solve the problem of redundant information and overlapping relations of the entity and relation extraction model, we propose a joint extraction model. This model can directly extract multiple pairs of related entities without generating unrelated redundant information. We also propose a recurrent neural network named Encoder-LSTM that enhances the ability of recurrent units to model sentences. Specifically, the joint model includes three sub-modules: the Named Entity Recognition sub-module consisted of a pre-trained language model and an LSTM decoder layer, the Entity Pair Extraction sub-module which uses Encoder-LSTM network to model the order relationship between related entity pairs, and the Relation Classification sub-module including Attention mechanism. We conducted experiments on the public datasets ADE and CoNLL04 to evaluate the effectiveness of our model. The results show that the proposed model achieves good performance in the task of entity and relation extraction and can greatly reduce the amount of redundant information.