Matching the Blanks: Distributional Similarity for Relation Learning

plkmo/BERT-Relation-Extraction • • ACL 2019

General purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction.

LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention

studio-ousia/luke • • EMNLP 2020

In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer.

Enriching Pre-trained Language Model with Entity Information for Relation Classification

monologg/R-BERT • • 20 May 2019

In this paper, we propose a model that both leverages the pre-trained BERT language model and incorporates information from the target entities to tackle the relation classification task.

Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing

roomylee/entity-aware-relation-classification • • 23 Jan 2019

Our model not only utilizes entities and their latent types as features effectively but also is more interpretable by visualizing attention mechanisms applied to our model and results of LET.

Advancing NLP with Cognitive Language Processing Signals

DS3Lab/zuco-nlp • 4 Apr 2019

Cognitive language processing data such as eye-tracking features have shown improvements on single NLP tasks.

UPB at SemEval-2020 Task 6: Pretrained Language Models for Definition Extraction

avramandrei/DeftEval • • SEMEVAL 2020

This work presents our contribution in the context of the 6th task of SemEval-2020: Extracting Definitions from Free Text in Textbooks (DeftEval).

Relation Classification via Convolutional Deep Neural Network

onehaitao/CNN-relation-extraction • • COLING 2014

Classifying Relations by Ranking with Convolutional Neural Networks

onehaitao/CR-CNN-relation-extraction • • IJCNLP 2015

Relation classification is an important semantic processing task for which state-ofthe-art systems still rely on costly handcrafted features.

End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures

tticoin/LSTM-ER • ACL 2016

We present a novel end-to-end neural model to extract entities and relations between them.

Large-scale Exploration of Neural Relation Classification Architectures

aidantee/MASS • • EMNLP 2018

Experimental performance on the task of relation classification has generally improved using deep neural network architectures.