https://github.com/anil1055/n-stage_LDA
공식 코드n-stage Latent Dirichlet Allocation: A Novel Approach for LDA
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/annabechang/Propaganda_Tech_Twitter_PRC
공식 코드https://github.com/anonymous-researcher-573/contrained_non-negative-matrix-factorization
공식 코드Constrained Non-negative Matrix Factorization for Guided Topic Modeling of Minority Topics
https://github.com/anpoc/language-models-in-medicine
공식 코드Evaluation of Language Models in the Medical Context Under Resource-Constrained Settings
https://github.com/ant-research/structuredlm_rtdt
공식 코드https://github.com/aoshuang92/confidence_calibration_cl
공식 코드Confidence-Aware Calibration and Scoring Functions for Curriculum Learning
https://github.com/apeterswu/Depth_Growing_NMT
공식 코드https://github.com/archanray/approximate_similarities
공식 코드https://github.com/architrathore/TopoAct
공식 코드TopoAct: Visually Exploring the Shape of Activations in Deep Learning
https://github.com/ariepratama/syntax-driven-da
공식 코드Syntax-driven Data Augmentation for Named Entity Recognition
https://github.com/arman-rayan-sharif/arman-text-emotion
공식 코드ArmanEmo: A Persian Dataset for Text-based Emotion Detection
https://github.com/arminarj/empathic-grounding
공식 코드https://github.com/arthurbra/sequential-sentence-classification
공식 코드Cross-Domain Multi-Task Learning for Sequential Sentence Classification in Research Papers
https://github.com/artitw/text2class
공식 코드https://github.com/arumaekawa/dilm
공식 코드DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation
https://github.com/arusl/anlp_nlp2021_d3-1
공식 코드An Experimental Evaluation of Japanese Tokenizers for Sentiment-Based Text Classification
https://github.com/asFeng/Diffuser
공식 코드Diffuser: Efficient Transformers with Multi-hop Attention Diffusion for Long Sequences
https://github.com/asappresearch/dynamic-classification
공식 코드https://github.com/asappresearch/sru
공식 코드https://github.com/ashfarhangi/Protoformer
공식 코드https://github.com/askerlee/topicvec
공식 코드Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs)
https://github.com/askerlee/vmfmix
공식 코드https://github.com/asking28/offenseval2020
공식 코드https://github.com/asking28/sentimix2020
공식 코드https://github.com/asmgx/llms
공식 코드Can Reasoning LLMs Enhance Clinical Document Classification?
https://github.com/atapour/rank-over-class
공식 코드Rank over Class: The Untapped Potential of Ranking in Natural Language Processing
https://github.com/atcemgil/bam
공식 코드https://github.com/aterenin/Parallel-HDP-Experiments
공식 코드Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models
https://github.com/atharva-naik/VADEC
공식 코드Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach
https://github.com/auotomaton/satiretr
공식 코드Make Satire Boring Again: Reducing Stylistic Bias of Satirical Corpus by Utilizing Generative LLMs
https://github.com/autonlab/KeyClass
공식 코드Classifying Unstructured Clinical Notes via Automatic Weak Supervision
https://github.com/av9ash/cognitive_mental_llm
공식 코드https://github.com/avisheklahiri/citeprompt
공식 코드CitePrompt: Using Prompts to Identify Citation Intent in Scientific Papers
https://github.com/awslabs/w-lda
공식 코드https://github.com/awwong1/topic-traceability
공식 코드https://github.com/aylai/GCDC-corpus
공식 코드Discourse Coherence in the Wild: A Dataset, Evaluation and Methods
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.