# CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases

arXiv: https://arxiv.org/abs/1610.08763 · HTML: https://mlsanity.com/paper/1610.08763/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2016.

## Built on by (most cited first, 28 of 28)

- [PTR: Prompt Tuning with Rules for Text Classification](https://mlsanity.com/paper/2105.11259/) (2021)
- [KG-RAG: Bridging the Gap Between Knowledge and Creativity](https://mlsanity.com/paper/2405.12035/) (2024)
- [PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction](https://mlsanity.com/paper/2106.09895/) (2021)
- [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023)
- [MATCH: Metadata-Aware Text Classification in A Large Hierarchy](https://mlsanity.com/paper/2102.07349/) (2021)
- [Revisiting the Negative Data of Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2105.10158/) (2021)
- [UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction](https://mlsanity.com/paper/2211.09039/) (2022)
- [Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey](https://mlsanity.com/paper/2103.16929/) (2021)
- [RadGraph2: Modeling Disease Progression in Radiology Reports via Hierarchical Information Extraction](https://mlsanity.com/paper/2308.05046/) (2023)
- [MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction](https://mlsanity.com/paper/2401.09839/) (2024)
- [Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model](https://mlsanity.com/paper/2106.09234/) (2021)
- [Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction](https://mlsanity.com/paper/2404.09593/) (2024)
- [A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition](https://mlsanity.com/paper/2101.00388/) (2021)
- [Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering](https://mlsanity.com/paper/2108.09689/) (2021)
- [Distantly-Supervised Joint Extraction with Noise-Robust Learning](https://mlsanity.com/paper/2310.04994/) (2023)
- [Enriched Attention for Robust Relation Extraction](https://mlsanity.com/paper/2104.10899/) (2021)
- [A Cascade Dual-Decoder Model for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2106.14163/) (2021)
- [Dual Pointer Network for Fast Extraction of Multiple Relations in a Sentence](https://mlsanity.com/paper/2103.03509/) (2021)
- [Techniques for Jointly Extracting Entities and Relations: A Survey](https://mlsanity.com/paper/2103.06118/) (2021)
- [Clustering and Network Analysis for the Embedding Spaces of Sentences and Sub-Sentences](https://mlsanity.com/paper/2110.00697/) (2021)
- [SpanRE: Entities and Overlapping Relations Extraction Based on Spans and Entity Attention](https://mlsanity.com/paper/2304.02901/) (2023)
- [Adaptive Ordered Information Extraction with Deep Reinforcement Learning](https://mlsanity.com/paper/2306.10787/) (2023)
- [Extracting Relational Triples Based on Graph Recursive Neural Network via Dynamic Feedback Forest Algorithm](https://mlsanity.com/paper/2308.11411/) (2023)
- [BitCoin: Bidirectional Tagging and Supervised Contrastive Learning based Joint Relational Triple Extraction Framework](https://mlsanity.com/paper/2309.11853/) (2023)
- [Relation Extraction Model Based on Semantic Enhancement Mechanism](https://mlsanity.com/paper/2311.02564/) (2023)
- [Bridge: A Unified Framework to Knowledge Graph Completion via Language Models and Knowledge Representation](https://mlsanity.com/paper/2411.06660/) (2024)
- [Inclusion of Role into Named Entity Recognition and Ranking](https://mlsanity.com/paper/2511.06886/) (2025)
- [SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2605.23440/) (2026)

## Abstract (arXiv)

Extracting entities and relations for types of interest from text is important for understanding massive text corpora. Traditionally, systems of entity relation extraction have relied on human-annotated corpora for training and adopted an incremental pipeline. Such systems require additional human expertise to be ported to a new domain, and are vulnerable to errors cascading down the pipeline. In this paper, we investigate joint extraction of typed entities and relations with labeled data heuristically obtained from knowledge bases (i.e., distant supervision). As our algorithm for type labeling via distant supervision is context-agnostic, noisy training data poses unique challenges for the task. 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. CoType, then using these learned embeddings, estimates the types of test (unlinkable) mentions. We formulate a joint optimization problem to learn embeddings from text corpora and knowledge bases, adopting a novel partial-label loss function for noisy labeled data and introducing an object "translation" function to capture the cross-constraints of entities and relations on each other. Experiments on three public datasets demonstrate the effectiveness of CoType across different domains (e.g., news, biomedical), with an average of 25% improvement in F1 score compared to the next best method.