mlsanityData to 14 Oct 2026

KE-QI: A Knowledge Enhanced Article Quality Identification Dataset

arXiv 2206.07556 ↗First posted 15 Jun 2022cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

With so many articles of varying qualities being produced every moment, it is a very urgent task to screen outstanding articles and commit them to social media. To our best knowledge, there is a lack of datasets and mature research works in identifying high-quality articles. Consequently, we conduct some surveys and finalize 7 objective indicators to annotate the quality of 10k articles. During annotation, we find that many characteristics of high-quality articles (e.g., background) rely more on extensive external knowledge than inner semantic information of articles. In response, we link extracted article entities to Baidu Encyclopedia, then propose Knowledge Enhanced article Quality Identification (KE-QI) dataset. To make better use of external knowledge, we propose a compound model which fuses the text and external knowledge information via a gate unit to classify the quality of an article. Our experimental results on KE-QI show that with initialization of our pre-trained Node2Vec model, our model achieves about 78% F1, outperforming other baselines.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 39 references that are in the map. First posted on arXiv 15 Jun 2022.

Builds on

29 of 39 references.

References · 29

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
  4. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
  5. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  6. 2016Variational Graph Auto-Encoderscited by 1,382
  7. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
  8. 2019Graph Neural Networks for Social Recommendationcited by 714
  9. 2019Graph U-Netscited by 492
  10. 2019MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixingcited by 443
  11. 2019KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representationcited by 356
  12. 2018Graph Convolutional Networks for Text Classificationcited by 345
17 more
  1. 2019K-BERT: Enabling Language Representation with Knowledge Graphcited by 304
  2. 2019ERNIE: Enhanced Representation through Knowledge Integrationcited by 302
  3. 2019Knowledge Enhanced Contextual Word Representationscited by 296
  4. 2019Position-aware Graph Neural Networkscited by 203
  5. 2020Deep Learning Based Text Classification: A Comprehensive Reviewcited by 141
  6. 2017Bilateral Multi-Perspective Matching for Natural Language Sentencescited by 121
  7. 2019Multi-hop Reading Comprehension through Question Decomposition and Rescoringcited by 119
  8. 2021BertGCN: Transductive Text Classification by Combining GCN and BERTcited by 74
  9. 2017Graph Convolutional Encoders for Syntax-aware Neural Machine Translationcited by 70
  10. 2019Cognitive Graph for Multi-Hop Reading Comprehension at Scalecited by 65
  11. 2018Graph-to-Sequence Learning using Gated Graph Neural Networkscited by 61
  12. 2019Text Level Graph Neural Network for Text Classificationcited by 60
  13. 2019DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classificationcited by 59
  14. 2021Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checkingcited by 52
  15. 2015A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representationscited by 32
  16. 2021Evaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Taskscited by 32
  17. 2017Do Convolutional Networks need to be Deep for Text Classification ?cited by 11

Built on by

Not cited yet.