# Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path

arXiv: https://arxiv.org/abs/1508.03720 · HTML: https://mlsanity.com/paper/1508.03720/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2015.

## Built on by (most cited first, 30 of 35)

- [Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees](https://mlsanity.com/paper/2103.04350/) (2021)
- [Prototypical Representation Learning for Relation Extraction](https://mlsanity.com/paper/2103.11647/) (2021)
- [A Survey on Extraction of Causal Relations from Natural Language Text](https://mlsanity.com/paper/2101.06426/) (2021)
- [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023)
- [Document-Level Relation Extraction with Sentences Importance Estimation and Focusing](https://mlsanity.com/paper/2204.12679/) (2022)
- [Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling](https://mlsanity.com/paper/2305.11719/) (2023)
- [Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey](https://mlsanity.com/paper/2103.16929/) (2021)
- [Deep Neural Network Based Relation Extraction: An Overview](https://mlsanity.com/paper/2101.01907/) (2021)
- [Improving Distantly-Supervised Relation Extraction through BERT-based Label & Instance Embeddings](https://mlsanity.com/paper/2102.01156/) (2021)
- [Clinical Relation Extraction Using Transformer-based Models](https://mlsanity.com/paper/2107.08957/) (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)
- [It Takes Two Flints to Make a Fire: Multitask Learning of Neural Relation and Explanation Classifiers](https://mlsanity.com/paper/2204.11424/) (2022)
- [Event-based clinical findings extraction from radiology reports with pre-trained language model](https://mlsanity.com/paper/2112.13512/) (2021)
- [Do Models Learn the Directionality of Relations? A New Evaluation: Relation Direction Recognition](https://mlsanity.com/paper/2105.09045/) (2021)
- [Nanopublication-Based Semantic Publishing and Reviewing: A Field Study with Formalization Papers](https://mlsanity.com/paper/2203.01608/) (2022)
- [Enriched Attention for Robust Relation Extraction](https://mlsanity.com/paper/2104.10899/) (2021)
- [A Neural Edge-Editing Approach for Document-Level Relation Graph Extraction](https://mlsanity.com/paper/2106.09900/) (2021)
- [Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction](https://mlsanity.com/paper/2109.05182/) (2021)
- [Identifying causal relations in tweets using deep learning: Use case on diabetes-related tweets from 2017-2021](https://mlsanity.com/paper/2111.01225/) (2021)
- [Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networks](https://mlsanity.com/paper/2111.10545/) (2021)
- [Modeling Multi-Granularity Hierarchical Features for Relation Extraction](https://mlsanity.com/paper/2204.04437/) (2022)
- [A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representations](https://mlsanity.com/paper/2311.00923/) (2023)
- [A Global-Local Attention Mechanism for Relation Classification](https://mlsanity.com/paper/2407.01424/) (2024)
- [Entity-Aware Self-Attention and Contextualized GCN for Enhanced Relation Extraction in Long Sentences](https://mlsanity.com/paper/2409.13755/) (2024)
- [Exploiting Rich Syntax for Better Knowledge Base Question Answering](https://mlsanity.com/paper/2107.07940/) (2021)
- [Multi-Scale Feature and Metric Learning for Relation Extraction](https://mlsanity.com/paper/2107.13425/) (2021)
- [Global alignment for relation extraction in Microbiology](https://mlsanity.com/paper/2112.02097/) (2021)
- [Automatically Generating Counterfactuals for Relation Classification](https://mlsanity.com/paper/2202.10668/) (2022)
- [Retrieval of Scientific and Technological Resources for Experts and Scholars](https://mlsanity.com/paper/2204.06142/) (2022)
- [A Sequence Tagging based Framework for Few-Shot Relation Extraction](https://mlsanity.com/paper/2208.08053/) (2022)

## Abstract (arXiv)

Relation classification is an important research arena in the field of natural language processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the relation of two entities in a sentence. Our neural architecture leverages the shortest dependency path (SDP) between two entities; multichannel recurrent neural networks, with long short term memory (LSTM) units, pick up heterogeneous information along the SDP. Our proposed model has several distinct features: (1) The shortest dependency paths retain most relevant information (to relation classification), while eliminating irrelevant words in the sentence. (2) The multichannel LSTM networks allow effective information integration from heterogeneous sources over the dependency paths. (3) A customized dropout strategy regularizes the neural network to alleviate overfitting. We test our model on the SemEval 2010 relation classification task, and achieve an F1-score of 83.7%, higher than competing methods in the literature.