# Improved Relation Extraction with Feature-Rich Compositional Embedding Models

arXiv: https://arxiv.org/abs/1505.02419 · HTML: https://mlsanity.com/paper/1505.02419/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2015.

## Built on by (most cited first, 12 of 12)

- [ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT](https://mlsanity.com/paper/2302.10205/) (2023)
- [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023)
- [What do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification](https://mlsanity.com/paper/2204.13516/) (2022)
- [Revisiting the Negative Data of Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2105.10158/) (2021)
- [SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues](https://mlsanity.com/paper/2106.01006/) (2021)
- [Less is More: Rethinking State-of-the-art Continual Relation Extraction Models with a Frustratingly Easy but Effective Approach](https://mlsanity.com/paper/2209.00243/) (2022)
- [A Cascade Dual-Decoder Model for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2106.14163/) (2021)
- [Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning](https://mlsanity.com/paper/2110.08254/) (2021)
- [Multiple Relations Classification using Imbalanced Predictions Adaptation](https://mlsanity.com/paper/2309.13718/) (2023)
- [Dual Pointer Network for Fast Extraction of Multiple Relations in a Sentence](https://mlsanity.com/paper/2103.03509/) (2021)
- [Budget Sensitive Reannotation of Noisy Relation Classification Data Using Label Hierarchy](https://mlsanity.com/paper/2112.13320/) (2021)
- [A Decoupling and Aggregating Framework for Joint Extraction of Entities and Relations](https://mlsanity.com/paper/2405.08311/) (2024)

## Abstract (arXiv)

Compositional embedding models build a representation (or embedding) for a linguistic structure based on its component word embeddings. We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement. The key idea is to combine both (unlexicalized) hand-crafted features with learned word embeddings. The model is able to directly tackle the difficulties met by traditional compositional embeddings models, such as handling arbitrary types of sentence annotations and utilizing global information for composition. We test the proposed model on two relation extraction tasks, and demonstrate that our model outperforms both previous compositional models and traditional feature rich models on the ACE 2005 relation extraction task, and the SemEval 2010 relation classification task. The combination of our model and a log-linear classifier with hand-crafted features gives state-of-the-art results.