LightRel SemEval-2018 Task 7: Lightweight and Fast Relation Classification

04/19/2018

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We present LightRel, a lightweight and fast relation classifier. Our goal is to develop a high baseline for different relation extraction tasks. By defining only very few data-internal, word-level features and external knowledge sources in the form of word clusters and word embeddings, we train a fast and simple linear classifier.

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LightRel SemEval-2018 Task 7: Lightweight and Fast Relation Classification

Task-Oriented Learning of Word Embeddings for Semantic Relation Classification

Improved Relation Extraction with Feature-Rich Compositional Embedding Models

Word-Level Loss Extensions for Neural Temporal Relation Classification

Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings

The performance evaluation of Multi-representation in the Deep Learning models for Relation Extraction Task

Exploring the Suitability of Semantic Spaces as Word Association Models for the Extraction of Semantic Relationships

On the effectiveness of feature set augmentation using clusters of word embeddings

LightRel SemEval-2018 Task 7: Lightweight and Fast Relation Classification

Related Research

Task-Oriented Learning of Word Embeddings for Semantic Relation Classification

Improved Relation Extraction with Feature-Rich Compositional Embedding Models

Word-Level Loss Extensions for Neural Temporal Relation Classification

Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings

The performance evaluation of Multi-representation in the Deep Learning models for Relation Extraction Task

Exploring the Suitability of Semantic Spaces as Word Association Models for the Extraction of Semantic Relationships

On the effectiveness of feature set augmentation using clusters of word embeddings