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natural-language-processing-6

Relation Classification

3260 papers • 126 benchmarks • 313 datasets

Relation Classification is the task of identifying the semantic relation holding between two nominal entities in text. Source: Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text

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Benchmarks

These leaderboards are used to track progress in relation-classification-13

Trend

Dataset

Best Model

Actions

TACRED

TACRED

SemEval 2010 Task 8

SemEval 2010 Task 8

FewRel

FewRel

Libraries

i

Use these libraries to find relation-classification-13 models and implementations

Datasets

TACRED

FewRel

FewRel 2.0

MATRES

CDCP

GUM

Subtasks

Few-Shot Relation Classification Implicit Discourse Relation Classification Cause-Effect Relation Classification

Most implemented papers

Matching the Blanks: Distributional Similarity for Relation Learning

T. Kwiatkowski, Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling•Thu Jun 06 2019

This paper builds on extensions of Harris’ distributional hypothesis to relations, as well as recent advances in learning text representations (specifically, BERT), to build task agnostic relation representations solely from entity-linked text.

838

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

CDCP

CDCP

AbstRCT - Neoplasm

AbstRCT - Neoplasm

DRI Corpus

DRI Corpus

Discovery Dataset

Discovery Dataset

MATRES

MATRES

RELX

RELX

LabPics

DISRPT2021

0

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

Akari Asai, Yuji Matsumoto, Hideaki Takeda, Ikuya Yamada, Hiroyuki Shindo•Thu Oct 01 2020

New pretrained contextualized representations of words and entities based on the bidirectional transformer, and an entity-aware self-attention mechanism that considers the types of tokens (words or entities) when computing attention scores are proposed.

735 0

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

Shanchan Wu, Yifan He•Sun May 19 2019

This paper proposes a model that both leverages the pre-trained BERT language model and incorporates information from the target entities to tackle the relation classification task and achieves significant improvement over the state-of-the-art method on the SemEval-2010 task 8 relational dataset.

447 0

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

Joohong Lee, Sang-gyu Seo, Y. Choi•Tue Jan 22 2019

This work proposes a novel end-to-end recurrent neural model which incorporates an entity-aware attention mechanism with a latent entity typing (LET) method and demonstrates that the model outperforms existing state-of-the-art models without any high-level features.

119 0

Advancing NLP with Cognitive Language Processing Signals

N. Langer, Nora Hollenstein, Maria Barrett, Marius Troendle, Francesco Bigiolli, Ce Zhang•Wed Apr 03 2019

This work uses gaze and EEG features to augment models of named entity recognition, relation classification, and sentiment analysis and shows the potential and current limitations of employing human language processing data for NLP.

41 0

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

Andrei-Marius Avram, Dumitru-Clementin Cercel, Costin-Gabriel Chiru•Thu Sep 10 2020

This work uses various pretrained language models (i.e., BERT, XLNet, RoBERTa, SciBERT, and ALBERT) to solve each of the three subtasks of the DeftEval competition, and explores a multi-task architecture that was trained to jointly predict the outputs for the second and the third subtasks.

7 0

Relation Classification via Convolutional Deep Neural Network

Guangyou Zhou, Kang Liu, Jun Zhao, Daojian Zeng, Siwei Lai•Thu Jul 31 2014

This paper exploits a convolutional deep neural network (DNN) to extract lexical and sentence level features from the output of pre-existing natural language processing systems and significantly outperforms the state-of-the-art methods.

1737 0

Classifying Relations by Ranking with Convolutional Neural Networks

Bowen Zhou, C. D. Santos, Bing Xiang•Thu Apr 23 2015

This work proposes a new pairwise ranking loss function that makes it easy to reduce the impact of artificial classes and shows that it is more effective than CNN followed by a softmax classifier and using only word embeddings as input features is enough to achieve state-of-the-art results.

591 0

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

Mohit Bansal, Makoto Miwa•Mon Jan 04 2016

A novel end-to-end neural model to extract entities and relations between them and compares favorably to the state-of-the-art CNN based model (in F1-score) on nominal relation classification (SemEval-2010 Task 8).

1248 0

Large-scale Exploration of Neural Relation Classification Architectures

Mohammad Taher Pilehvar, Nigel Collier, Hoang-Quynh Le, Duy-Cat Can, S. T. Vu, T. Dang•Sun Dec 31 2017

This work presents a systematic large-scale analysis of neural relation classification architectures on six benchmark datasets with widely varying characteristics, and proposes a novel multi-channel LSTM model combined with a CNN that takes advantage of all currently popular linguistic and architectural features.

15 0

Adding a benchmark result helps the community track progress.

Relation Classification | State-of-the-Art