https://github.com/HX-idiot/Hybrid_Attention_XML
공식 코드Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification
Papers with Code 아카이브
Text Classification
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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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/HenryPengZou/DeCrisisMB
공식 코드DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank
https://github.com/HenryPengZou/JointMatch
공식 코드https://github.com/HeoTaksung/DAGAM--Data-Augmentation-with-Generation-And-Modification
공식 코드https://github.com/HeoTaksung/Document-to-Sequence-BERT
공식 코드Medical Code Prediction from Discharge Summary: Document to Sequence BERT using Sequence Attention
https://github.com/HoseinAzarbonyad/HiTR
공식 코드HiTR: Hierarchical Topic Model Re-estimation for Measuring Topical Diversity of Documents
https://github.com/Hsuan-Tung/universal_attack_natural_trigger
공식 코드Universal Adversarial Attacks with Natural Triggers for Text Classification
https://github.com/IBM/SPAHM
공식 코드https://github.com/IBM/WordMoversEmbeddings
공식 코드https://github.com/INGEOTEC/microTC
공식 코드An Automated Text Categorization Framework based on Hyperparameter Optimization
https://github.com/INK-USC/NExT
공식 코드https://github.com/INK-USC/expl-refinement
공식 코드https://github.com/IamAdiSri/hcms-semeval20
공식 코드HCMS at SemEval-2020 Task 9: A Neural Approach to Sentiment Analysis for Code-Mixed Texts
https://github.com/IllinoisLiteraryLab/novelty
공식 코드https://github.com/Imtiazkarimik23/SPEC5G
공식 코드https://github.com/IsaacChanghau/DeepNaryECOC
공식 코드https://github.com/Ishani-Mondal/BBAEG
공식 코드BBAEG: Towards BERT-based Biomedical Adversarial Example Generation for Text Classification
https://github.com/IsraelAbebe/An-Amharic-News-Text-classification-Dataset
공식 코드https://github.com/IvonaTau/emotionaldan
공식 코드https://github.com/IyatomiLab/CE-CLCNN
공식 코드End-to-End Text Classification via Image-based Embedding using Character-level Networks
https://github.com/IyatomiLab/GDCE-SSA
공식 코드https://github.com/JAugusto97/ToLD-Br
공식 코드https://github.com/JHL-HUST/FGPM
공식 코드https://github.com/JaTrev/unsupervised_graph-based
공식 코드GraphTMT: Unsupervised Graph-based Topic Modeling from Video Transcripts
https://github.com/James-Yip/QuantiDCE
공식 코드https://github.com/JieyuZ2/wrench/blob/main/wrench/endmodel/ars2.py
공식 코드Adaptive Ranking-based Sample Selection for Weakly Supervised Class-imbalanced Text Classification
https://github.com/JingqingZ/KG4ZeroShotText
공식 코드Integrating Semantic Knowledge to Tackle Zero-shot Text Classification
https://github.com/Joaoffg/CAALM
공식 코드Combining Autoregressive and Autoencoder Language Models for Text Classification
https://github.com/JohnTailor/tkm
공식 코드https://github.com/JumanaFM/emoviz
공식 코드Speech-Based Emotion Recognition using Neural Networks and Information Visualization
https://github.com/KLUE-benchmark/KLUE
공식 코드https://github.com/Kaleidophon/deep-significance
공식 코드https://github.com/Kaylee0501/X-MLClass
공식 코드Open-world Multi-label Text Classification with Extremely Weak Supervision
https://github.com/Khoat/OPE
공식 코드https://github.com/KomeijiForce/TextGrafting
공식 코드Text Grafting: Near-Distribution Weak Supervision for Minority Classes in Text Classification
https://github.com/KurdishBLARK/HawramiClassification
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.