# Multiple Relations Classification using Imbalanced Predictions Adaptation

arXiv: https://arxiv.org/abs/2309.13718 · HTML: https://mlsanity.com/paper/2309.13718/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 41 references that are in the map.
- First posted on arXiv 24 Sep 2023.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://mlsanity.com/paper/1603.06393/) (2016)
- [Structured Prediction as Translation between Augmented Natural Languages](https://mlsanity.com/paper/2101.05779/) (2021)
- [Dice Loss for Data-imbalanced NLP Tasks](https://mlsanity.com/paper/1911.02855/) (2019)
- [Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://mlsanity.com/paper/1809.10185/) (2018)
- [Fast WordPiece Tokenization](https://mlsanity.com/paper/2012.15524/) (2020)
- [Enriching Pre-trained Language Model with Entity Information for Relation Classification](https://mlsanity.com/paper/1905.08284/) (2019)
- [CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning](https://mlsanity.com/paper/1911.10438/) (2019)
- [Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers](https://mlsanity.com/paper/1902.01030/) (2019)
- [Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing](https://mlsanity.com/paper/1901.08163/) (2019)
- [Improved Relation Extraction with Feature-Rich Compositional Embedding Models](https://mlsanity.com/paper/1505.02419/) (2015)
- [Zero-shot Relation Classification from Side Information](https://mlsanity.com/paper/2011.07126/) (2020)
- [Downstream Model Design of Pre-trained Language Model for Relation Extraction Task](https://mlsanity.com/paper/2004.03786/) (2020)

## Built on by (most cited first, 1 of 1)

- [Knowledge Graph Completion using Structural and Textual Embeddings](https://mlsanity.com/paper/2404.16206/) (2024)

## Abstract (arXiv)

The relation classification task assigns the proper semantic relation to a pair of subject and object entities; the task plays a crucial role in various text mining applications, such as knowledge graph construction and entities interaction discovery in biomedical text. Current relation classification models employ additional procedures to identify multiple relations in a single sentence. Furthermore, they overlook the imbalanced predictions pattern. The pattern arises from the presence of a few valid relations that need positive labeling in a relatively large predefined relations set. We propose a multiple relations classification model that tackles these issues through a customized output architecture and by exploiting additional input features. Our findings suggest that handling the imbalanced predictions leads to significant improvements, even on a modest training design. The results demonstrate superiority performance on benchmark datasets commonly used in relation classification. To the best of our knowledge, this work is the first that recognizes the imbalanced predictions within the relation classification task.