109 papers with code • 8 benchmarks • 17 datasets
Relation Classification is the task of identifying the semantic relation holding between two nominal entities in text.
AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification
In this study, we introduce an interpretable graph-based deep learning prediction model, AttentionSiteDTI, which utilizes protein binding sites along with a self-attention mechanism to address the problem of drug–target interaction prediction.
Sentence-level relation extraction (RE) has a highly imbalanced data distribution that about 80% of data are labeled as negative, i. e., no relation; and there exist minority classes (MC) among positive labels; furthermore, some of MC instances have an incorrect label.
Compared with traditional sentence-level relation extraction, document-level relation extraction is a more challenging task where an entity in a document may be mentioned multiple times and associated with multiple relations.
Relation classification models are conventionally evaluated using only a single measure, e. g., micro-F1, macro-F1 or AUC.
Semantic typing aims at classifying tokens or spans of interest in a textual context into semantic categories such as relations, entity types, and event types.
What do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification
Over the last five years, research on Relation Extraction (RE) witnessed extensive progress with many new dataset releases.
To achieve this goal, our work addresses the problems of subevent relation extraction (SRE) and temporal event relation extraction (TRE) that aim to predict subevent and temporal relations between two given event mentions/triggers in texts.
RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction
We introduce the task setting of Zero-Shot Relation Triplet Extraction (ZeroRTE) to encourage further research in low-resource relation extraction methods.