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Text Classification

583 평가 기록 191 datasets

Text Classification is the task of assigning a sentence or document an appropriate category. The categories depend on the chosen dataset and can range from topics.

Text Classification problems include emotion classification, news classification, citation intent classification, among others. Benchmark datasets for evaluating text classification capabilities include GLUE, AGNews, among others.

In recent years, deep learning techniques like XLNet and RoBERTa have attained some of the biggest performance jumps for text classification problems.

<span style="color:grey; opacity: 0.6">( Image credit: Text Classification Algorithms: A Survey )</span>

하위 태스크

DC Document Classification 22 데이터셋 · 51 rows MT Multi-Label Text Classification 20 데이터셋 · 50 rows TC Toxic Comment Classification 4 데이터셋 · 25 rows TM Topic Models 9 데이터셋 · 23 rows EC Emotion Classification 9 데이터셋 · 19 rows FT Few-Shot Text Classification 8 데이터셋 · 19 rows CI Citation Intent Classification 2 데이터셋 · 16 rows SC Sentence Classification 6 데이터셋 · 16 rows CE Coherence Evaluation 2 데이터셋 · 7 rows UT Unsupervised Text Classification 4 데이터셋 · 7 rows ST Semi-Supervised Text Classification 2 데이터셋 · 6 rows VD Variable Detection 1 데이터셋 · 2 rows HT Hierarchical Text Classification of Blurbs (GermEval 2019) 1 데이터셋 · 1 rows CT Cross-Domain Text Classification 0 데이터셋 · 0 rows SD Satire Detection 0 데이터셋 · 0 rows TC Text Categorization 0 데이터셋 · 0 rows