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Word Embeddings
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Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.
Techniques for learning word embeddings can include Word2Vec, GloVe, and other neural network-based approaches that train on an NLP task such as language modeling or document classification.
<span style="color:grey; opacity: 0.6">( Image credit: Dynamic Word Embedding for Evolving Semantic Discovery )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Kinetikm/fasttextRelearnExperiment
https://github.com/KylianvG/Embetter
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
https://github.com/LAEarnsChamp/skipgram-negativesampling
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method
https://github.com/LouisTernon/NLP
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method
https://github.com/LuongPhan/UIT-ViSFD
SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence
https://github.com/M155K4R4/fastText
https://github.com/MI2DataLab/memr
Interpretable Segmentation of Medical Free-Text Records Based on Word Embeddings
https://github.com/MJafarMashhadi/Haplophysh
URLNet: Learning a URL Representation with Deep Learning for Malicious URL Detection
https://github.com/MJahangeerQureshi/Text-Classification
Subword Semantic Hashing for Intent Classification on Small Datasets
https://github.com/MLBurnham/word_embeddings
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method
https://github.com/ManuelVs/NNForTextClassification
Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents
https://github.com/ManuelVs/NeuralNetworks
Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents
https://github.com/Megvii-Nanjing/ML_GCN
Multi-Label Image Recognition with Graph Convolutional Networks
https://github.com/MhYao2014/HyperbolicEmbedding
https://github.com/MindCode-4/code-2/tree/main/persimmon
Transformers without Tears: Improving the Normalization of Self-Attention
https://github.com/MindCode-4/code-6/tree/main/alibi
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
https://github.com/MindSpore-paper-code-2/code399/tree/main/dscnn
Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents
https://github.com/MindSpore-paper-code-3/code9/tree/main/fasttext
https://github.com/MindSpore-scientific-2/code-8/tree/main/rand_conv-master
Understanding Composition of Word Embeddings via Tensor Decomposition
https://github.com/MindSpore-scientific/code-12/tree/main/AGW
Multilingual Jointly Trained Acoustic and Written Word Embeddings
https://github.com/MindSpore-scientific/code-3/tree/main/alibi
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
https://github.com/MirkoLenz/ReCAP-Argument-Graph-Retrieval
https://github.com/MiuLab/xSense
https://github.com/NanaLange/CL-HAABSA
https://github.com/NathanDuran/Sentence-Encoding-for-DA-Classification
ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
https://github.com/NilsRethmeier/MoRTy
How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks
https://github.com/Nim-NLP/fastText
https://github.com/Omerktn/fastText-iterative
https://github.com/PrashantRanjan09/Improved-Word-Embeddings
Improving the Accuracy of Pre-trained Word Embeddings for Sentiment Analysis
https://github.com/PyENE/meta-word-embedding
How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks
https://github.com/RadomirPopovicFON/Joint-Multiclass-Debiasing-of-Word-Embeddings
https://github.com/Ramos-Ramos/fruit-fly-net
https://github.com/RasaHQ/whatlies
Parallax: Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae
https://github.com/ReCAP-UTR/Argument-Graph-Retrieval
https://github.com/Ritam9/Stability-Analysis-of-Word-Embeddings
Factors Influencing the Surprising Instability of Word Embeddings
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.