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Relation Extraction On Semeval 2018 Task 10

Metrics

F1-Score

Results

Performance results of various models on this benchmark

Paper Title
SVM with GloVe0.76SUNNYNLP at SemEval-2018 Task 10: A Support-Vector-Machine-Based Method for Detecting Semantic Difference using Taxonomy and Word Embedding Features
SVM with ConceptNet, Wikipedia articles and WordNet synonyms0.74Luminoso at SemEval-2018 Task 10: Distinguishing Attributes Using Text Corpora and Relational Knowledge
Gradient boosting with co-occurrence count features and JoBimText features0.73BomJi at SemEval-2018 Task 10: Combining Vector-, Pattern- and Graph-based Information to Identify Discriminative Attributes
LexVec, word co-occurrence, and ConceptNet data combined using maximum entropy classifier0.72UWB at SemEval-2018 Task 10: Capturing Discriminative Attributes from Word Distributions
Use of Wikipedia and ConceptNet Transp. (No expl.)0.69ELiRF-UPV at SemEval-2018 Task 10: Capturing Discriminative Attributes with Knowledge Graphs and Wikipedia
Composes explicit vector spaces from WordNet Definitions, ConceptNet and Visual Genome0.69Identifying and Explaining Discriminative Attributes
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Relation Extraction On Semeval 2018 Task 10 | SOTA | HyperAI