TasksSotADatasetsPapersMethodsSubmitAbout
Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable BenchmarksAll SotADatasetsPapersMethods

Community

Submit ResultsAbout

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/DE-CNN

DE-CNN

Reported on 12 benchmarks across 3 tasks · 1 paper · 12 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing12 results

  • Sentiment AnalysisonSemEval 2016 Task 5 Sub Task 1 Slot 2
    Restaurant (F1)· 2018-05-11
    74.37
    SOTA
    Double Embeddings and CNN-based Sequence Labeling for Aspect ExtractionarXiv:1805.04601
  • Sentiment AnalysisonSemEval-2014 Task-4
    Restaurant (F1)· 2018-05-11
    85.2
    best: 92.76 (InstructABSA)
    SOTA
    Double Embeddings and CNN-based Sequence Labeling for Aspect ExtractionarXiv:1805.04601
  • Sentiment AnalysisonSemEval 2015 Task 12
    Restaurant (F1)· 2018-05-11
    68.28
    SOTA
    Double Embeddings and CNN-based Sequence Labeling for Aspect ExtractionarXiv:1805.04601
  • Sentiment AnalysisonSemEval 2014 Task 4 Sub Task 1
    Laptop (F1)· 2018-05-11
    81.59
    best: 92.3 (InstructABSA)
    SOTA
    Double Embeddings and CNN-based Sequence Labeling for Aspect ExtractionarXiv:1805.04601
  • Aspect-Based Sentiment Analysis (ABSA)onSemEval 2016 Task 5 Sub Task 1 Slot 2
    Restaurant (F1)· 2018-05-11
    74.37
    SOTA
    Double Embeddings and CNN-based Sequence Labeling for Aspect ExtractionarXiv:1805.04601
  • Aspect-Based Sentiment Analysis (ABSA)onSemEval-2014 Task-4
    Restaurant (F1)· 2018-05-11
    85.2
    best: 92.76 (InstructABSA)
    SOTA
    Double Embeddings and CNN-based Sequence Labeling for Aspect ExtractionarXiv:1805.04601
  • Aspect-Based Sentiment Analysis (ABSA)onSemEval 2015 Task 12
    Restaurant (F1)· 2018-05-11
    68.28
    SOTA
    Double Embeddings and CNN-based Sequence Labeling for Aspect ExtractionarXiv:1805.04601
  • Aspect-Based Sentiment Analysis (ABSA)onSemEval 2014 Task 4 Sub Task 1
    Laptop (F1)· 2018-05-11
    81.59
    best: 92.3 (InstructABSA)
    SOTA
    Double Embeddings and CNN-based Sequence Labeling for Aspect ExtractionarXiv:1805.04601
  • Aspect ExtractiononSemEval 2016 Task 5 Sub Task 1 Slot 2
    Restaurant (F1)· 2018-05-11
    74.37
    SOTA
    Double Embeddings and CNN-based Sequence Labeling for Aspect ExtractionarXiv:1805.04601
  • Aspect ExtractiononSemEval-2014 Task-4
    Restaurant (F1)· 2018-05-11
    85.2
    best: 92.76 (InstructABSA)
    SOTA
    Double Embeddings and CNN-based Sequence Labeling for Aspect ExtractionarXiv:1805.04601
  • Aspect ExtractiononSemEval 2015 Task 12
    Restaurant (F1)· 2018-05-11
    68.28
    SOTA
    Double Embeddings and CNN-based Sequence Labeling for Aspect ExtractionarXiv:1805.04601
  • Aspect ExtractiononSemEval 2014 Task 4 Sub Task 1
    Laptop (F1)· 2018-05-11
    81.59
    best: 92.3 (InstructABSA)
    SOTA
    Double Embeddings and CNN-based Sequence Labeling for Aspect ExtractionarXiv:1805.04601