# RTE: A Tool for Annotating Relation Triplets from Text

arXiv: https://arxiv.org/abs/2108.08184 · HTML: https://mlsanity.com/paper/2108.08184/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 18 Aug 2021.

## Builds on (most cited first, 11 of 11)

- [Position-Aware Tagging for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2010.02609/) (2020)
- [Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction](https://mlsanity.com/paper/1911.09886/) (2019)
- [Joint entity recognition and relation extraction as a multi-head selection problem](https://mlsanity.com/paper/1804.07847/) (2018)
- [A Hierarchical Framework for Relation Extraction with Reinforcement Learning](https://mlsanity.com/paper/1811.03925/) (2018)
- [RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information](https://mlsanity.com/paper/1812.04361/) (2018)
- [Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy](https://mlsanity.com/paper/1909.04273/) (2019)
- [Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention](https://mlsanity.com/paper/1804.06987/) (2018)
- [Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddings](https://mlsanity.com/paper/1904.07629/) (2019)
- [Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey](https://mlsanity.com/paper/2103.16929/) (2021)
- [Aspect Sentiment Triplet Extraction Using Reinforcement Learning](https://mlsanity.com/paper/2108.06107/) (2021)
- [Effective Attention Modeling for Neural Relation Extraction](https://mlsanity.com/paper/1912.03832/) (2019)

## Built on by (most cited first, 5 of 5)

- [MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction](https://mlsanity.com/paper/2401.09839/) (2024)
- [Text Takes Over: A Study of Modality Bias in Multimodal Intent Detection](https://mlsanity.com/paper/2508.16122/) (2025)
- [Analyzing Sentiment Polarity Reduction in News Presentation through Contextual Perturbation and Large Language Models](https://mlsanity.com/paper/2402.02145/) (2024)
- [Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documents](https://mlsanity.com/paper/2509.10935/) (2025)
- [IDALC: A Semi-Supervised Framework for Intent Detection and Active Learning based Correction](https://mlsanity.com/paper/2511.05921/) (2025)

## Abstract (arXiv)

In this work, we present a Web-based annotation tool `Relation Triplets Extractor' footnotehttps://abera87.github.io/annotate/ (RTE) for annotating relation triplets from the text. Relation extraction is an important task for extracting structured information about real-world entities from the unstructured text available on the Web. In relation extraction, we focus on binary relation that refers to relations between two entities. Recently, many supervised models are proposed to solve this task, but they mostly use noisy training data obtained using the distant supervision method. In many cases, evaluation of the models is also done based on a noisy test dataset. The lack of annotated clean dataset is a key challenge in this area of research. In this work, we built a web-based tool where researchers can annotate datasets for relation extraction on their own very easily. We use a server-less architecture for this tool, and the entire annotation operation is processed using client-side code. Thus it does not suffer from any network latency, and the privacy of the user's data is also maintained. We hope that this tool will be beneficial for the researchers to advance the field of relation extraction.