SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals

Abstract

In response to the continuing research interest in computational semantic analysis, we have proposed a new task for SemEval-2010: multi-way classification of mutually exclusive semantic relations between pairs of nominals. The task is designed to compare different approaches to the problem and to provide a standard testbed for future research. In this paper, we define the task, describe the creation of the datasets, and discuss the results of the participating 28 systems submitted by 10 teams.

semantic relations, nominals

Cited by in corpus (27)

• KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
• Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddings
• An evidential classifier based on Dempster-Shafer theory and deep learning
• KLUE: Korean Language Understanding Evaluation
• Classifying medical relations in clinical text via convolutional neural networks
• Guided Generation of Cause and Effect
• WRENCH: A Comprehensive Benchmark for Weak Supervision
• Large-Scale Noun Compound Interpretation Using Bootstrapping and the Web as a Corpus
• A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text
• Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning
• REFinD: Relation Extraction Financial Dataset
• Towards Interpretable Natural Language Understanding with Explanations as Latent Variables
• Automatic extraction of cause-effect-relations from requirements artifacts
• Guideline Learning for In-context Information Extraction
• Exploit Multiple Reference Graphs for Semi-supervised Relation Extraction
• Zero-shot Relation Classification from Side Information
• Enhancing Low-Resource Relation Representations through Multi-View Decoupling
• Local Boosting for Weakly-Supervised Learning
• Improving Relation Extraction by Leveraging Knowledge Graph Link Prediction
• An Empirical Study of Using Pre-trained BERT Models for Vietnamese Relation Extraction Task at VLSP 2020
• Deep Neural Networks for Relation Extraction
• Multi-Attribute Relation Extraction (MARE) -- Simplifying the Application of Relation Extraction
• Improving Cross-Domain Performance for Relation Extraction via Dependency Prediction and Information Flow Control
• Back to Prior Knowledge: Joint Event Causality Extraction via Convolutional Semantic Infusion
• An Annotated Corpus of Webtables for Information Extraction Tasks
• Open Rule Induction
• Method of Tibetan Person Knowledge Extraction