# Classifying Relations by Ranking with Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1504.06580 · HTML: https://mlsanity.com/paper/1504.06580/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2015.

## Built on by (most cited first, 30 of 33)

- [Prototypical Representation Learning for Relation Extraction](https://mlsanity.com/paper/2103.11647/) (2021)
- [Revisiting Large Language Models as Zero-shot Relation Extractors](https://mlsanity.com/paper/2310.05028/) (2023)
- [A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing](https://mlsanity.com/paper/2210.04675/) (2022)
- [Entity-centered Cross-document Relation Extraction](https://mlsanity.com/paper/2210.16541/) (2022)
- [Revisiting the Negative Data of Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2105.10158/) (2021)
- [Learning Logic Rules for Document-level Relation Extraction](https://mlsanity.com/paper/2111.05407/) (2021)
- [Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation](https://mlsanity.com/paper/2210.04497/) (2022)
- [KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction](https://mlsanity.com/paper/2106.00459/) (2021)
- [Deep Neural Network Based Relation Extraction: An Overview](https://mlsanity.com/paper/2101.01907/) (2021)
- [Innovations in Neural Data-to-text Generation: A Survey](https://mlsanity.com/paper/2207.12571/) (2022)
- [Reviewing Labels: Label Graph Network with Top-k Prediction Set for Relation Extraction](https://mlsanity.com/paper/2212.14270/) (2022)
- [Generic and Trend-aware Curriculum Learning for Relation Extraction in Graph Neural Networks](https://mlsanity.com/paper/2205.08625/) (2022)
- [Sequence Generation with Label Augmentation for Relation Extraction](https://mlsanity.com/paper/2212.14266/) (2022)
- [FastRE: Towards Fast Relation Extraction with Convolutional Encoder and Improved Cascade Binary Tagging Framework](https://mlsanity.com/paper/2205.02490/) (2022)
- [A Generative Model for Relation Extraction and Classification](https://mlsanity.com/paper/2202.13229/) (2022)
- [ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction](https://mlsanity.com/paper/2309.01370/) (2023)
- [RAKG:Document-level Retrieval Augmented Knowledge Graph Construction](https://mlsanity.com/paper/2504.09823/) (2025)
- [REKnow: Enhanced Knowledge for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2206.05123/) (2022)
- [Towards Better Graph-based Cross-document Relation Extraction via Non-bridge Entity Enhancement and Prediction Debiasing](https://mlsanity.com/paper/2406.16529/) (2024)
- [Do Models Learn the Directionality of Relations? A New Evaluation: Relation Direction Recognition](https://mlsanity.com/paper/2105.09045/) (2021)
- [Document-level Relation Extraction with Relation Correlations](https://mlsanity.com/paper/2212.10171/) (2022)
- [Knowledge-Driven Cross-Document Relation Extraction](https://mlsanity.com/paper/2405.13546/) (2024)
- [Mention-centered Graph Neural Network for Document-level Relation Extraction](https://mlsanity.com/paper/2103.08200/) (2021)
- [Integrating Heterogeneous Domain Information into Relation Extraction: A Case Study on Drug-Drug Interaction Extraction](https://mlsanity.com/paper/2212.10714/) (2022)
- [Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskrit](https://mlsanity.com/paper/2308.08807/) (2023)
- [Sentence Bag Graph Formulation for Biomedical Distant Supervision Relation Extraction](https://mlsanity.com/paper/2310.18912/) (2023)
- [Entity-Aware Self-Attention and Contextualized GCN for Enhanced Relation Extraction in Long Sentences](https://mlsanity.com/paper/2409.13755/) (2024)
- [Sent2Matrix: Folding Character Sequences in Serpentine Manifolds for Two-Dimensional Sentence](https://mlsanity.com/paper/2103.08387/) (2021)
- [SpanRE: Entities and Overlapping Relations Extraction Based on Spans and Entity Attention](https://mlsanity.com/paper/2304.02901/) (2023)
- [Dealing with negative samples with multi-task learning on span-based joint entity-relation extraction](https://mlsanity.com/paper/2309.09713/) (2023)

## Abstract (arXiv)

Relation classification is an important semantic processing task for which state-ofthe-art systems still rely on costly handcrafted features. In this work we tackle the relation classification task using a convolutional neural network that performs classification by ranking (CR-CNN). We propose a new pairwise ranking loss function that makes it easy to reduce the impact of artificial classes. We perform experiments using the the SemEval-2010 Task 8 dataset, which is designed for the task of classifying the relationship between two nominals marked in a sentence. Using CRCNN, we outperform the state-of-the-art for this dataset and achieve a F1 of 84.1 without using any costly handcrafted features. Additionally, our experimental results show that: (1) our approach is more effective than CNN followed by a softmax classifier; (2) omitting the representation of the artificial class Other improves both precision and recall; and (3) using only word embeddings as input features is enough to achieve state-of-the-art results if we consider only the text between the two target nominals.