Joint Entity and Relation Extraction
55 papers with code • 16 benchmarks • 16 datasets
Joint Entity and Relation Extraction is the task of extracting entity mentions and semantic relations between entities from unstructured text with a single model.
Datasets
Most implemented papers
Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles.
A General Framework for Information Extraction using Dynamic Span Graphs
We introduce a general framework for several information extraction tasks that share span representations using dynamically constructed span graphs.
Entity, Relation, and Event Extraction with Contextualized Span Representations
We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction.
Span-based Joint Entity and Relation Extraction with Transformer Pre-training
The model is trained using strong within-sentence negative samples, which are efficiently extracted in a single BERT pass.
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
We propose a novel domain-independent framework, called CoType, that runs a data-driven text segmentation algorithm to extract entity mentions, and jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces (for entity and relation mentions respectively), where, in each space, objects whose types are close will also have similar representations.
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
Joint extraction of entities and relations is an important task in information extraction.
Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders
In this work, we propose the novel {\em table-sequence encoders} where two different encoders -- a table encoder and a sequence encoder are designed to help each other in the representation learning process.
A Frustratingly Easy Approach for Entity and Relation Extraction
Our approach essentially builds on two independent encoders and merely uses the entity model to construct the input for the relation model.
Packed Levitated Marker for Entity and Relation Extraction
In particular, we propose a neighborhood-oriented packing strategy, which considers the neighbor spans integrally to better model the entity boundary information.
A sequence-to-sequence approach for document-level relation extraction
In this paper, we develop a sequence-to-sequence approach, seq2rel, that can learn the subtasks of DocRE (entity extraction, coreference resolution and relation extraction) end-to-end, replacing a pipeline of task-specific components.