RTE: A Tool for Annotating Relation Triplets from Text
Not on the map. Why?
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.
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
11 of 24 references.
References · 11
- 2020Position-Aware Tagging for Aspect Sentiment Triplet Extractioncited by 91
- 2019Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extractioncited by 73
- 2018Joint entity recognition and relation extraction as a multi-head selection problemcited by 59
- 2018A Hierarchical Framework for Relation Extraction with Reinforcement Learningcited by 49
- 2018RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Informationcited by 47
- 2019Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategycited by 41
- 2018Improving Distantly Supervised Relation Extraction using Word and Entity Based Attentioncited by 35
- 2019Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddingscited by 25
- 2021Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Surveycited by 12
- 2021Aspect Sentiment Triplet Extraction Using Reinforcement Learningcited by 12
- 2019Effective Attention Modeling for Neural Relation Extractioncited by 10
Built on by
Citing papers per year: 2024: 2; 2025: 3.
5 papers.
- 2024MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Constructioncited by 11
- 2025Text Takes Over: A Study of Modality Bias in Multimodal Intent Detectioncited by 7
- 2024Analyzing Sentiment Polarity Reduction in News Presentation through Contextual Perturbation and Large Language Modelscited by 1
- 2025Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documentscited by 1
- 2025IDALC: A Semi-Supervised Framework for Intent Detection and Active Learning based Correctioncited by 0
- 2025IDALC: A Semi-Supervised Framework for Intent Detection and Active Learning based Correction8 Nov 2025
- 2025Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documents13 Sep 2025
- 2025Text Takes Over: A Study of Modality Bias in Multimodal Intent Detection22 Aug 2025
- 2024Analyzing Sentiment Polarity Reduction in News Presentation through Contextual Perturbation and Large Language Models3 Feb 2024
- 2024MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction18 Jan 2024