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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/wendyqiu/diachronicbert
공식 코드HistBERT: A Pre-trained Language Model for Diachronic Lexical Semantic Analysis
https://github.com/wengsyx/low-resource-text-translation
공식 코드Towards Better Chinese-centric Neural Machine Translation for Low-resource Languages
https://github.com/wenhuchen/ETHZ-Bootstrapped-Captioning
공식 코드https://github.com/wenjiaxu/rs_scene_zsl
공식 코드Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene Classification
https://github.com/wenjiaxu/vgse
공식 코드VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot Learning
https://github.com/wenliangdai/Modality-Transferable-MER
공식 코드Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion Recognition
https://github.com/wolferobert3/adolescent-representation-bias
공식 코드Representation Bias of Adolescents in AI: A Bilingual, Bicultural Study
https://github.com/wolferobert3/ml-eat
공식 코드ML-EAT: A Multilevel Embedding Association Test for Interpretable and Transparent Social Science
https://github.com/wolferobert3/vast_aaai_2022
공식 코드VAST: The Valence-Assessing Semantics Test for Contextualizing Language Models
https://github.com/woojinchung/lms
공식 코드https://github.com/xavierfav/ae-w2v-attention
공식 코드Learning Contextual Tag Embeddings for Cross-Modal Alignment of Audio and Tags
https://github.com/xfold/LanguageBiasesInReddit
공식 코드https://github.com/xfold/discoveringandinterpretingconceptualbiases
공식 코드Discovering and Interpreting Biased Concepts in Online Communities
https://github.com/ximingwen/gaprotonet
공식 코드https://github.com/xmc-aalto/inceptionxml
공식 코드https://github.com/xpq-tech/swea
공식 코드SWEA: Updating Factual Knowledge in Large Language Models via Subject Word Embedding Altering
https://github.com/xrc10/unsup-cross-lingual-embedding-transfer
공식 코드Unsupervised Cross-lingual Transfer of Word Embedding Spaces
https://github.com/xud-yan/Trident
공식 코드Leveraging MLLM Embeddings and Attribute Smoothing for Compositional Zero-Shot Learning
https://github.com/yandex-research/graph-glove
공식 코드Embedding Words in Non-Vector Space with Unsupervised Graph Learning
https://github.com/yc-li20/Crossmodal_AEC
공식 코드https://github.com/yeliu918/QREFINE-PPO
공식 코드Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System
https://github.com/yeraidm/bridge2vec
공식 코드https://github.com/yeraidm/meemi
공식 코드Improving Cross-Lingual Word Embeddings by Meeting in the Middle
https://github.com/yftah89/Neural-SCL-Domain-Adaptation
공식 코드Neural Structural Correspondence Learning for Domain Adaptation
https://github.com/yftah89/Neural-SCLDomain-Adaptation
공식 코드Neural Structural Correspondence Learning for Domain Adaptation
https://github.com/yhcc/BertForRD
공식 코드https://github.com/yixinw-lab/attention-uai
공식 코드Bidirectional Attention as a Mixture of Continuous Word Experts
https://github.com/ymgw55/Axis-Tour
공식 코드Axis Tour: Word Tour Determines the Order of Axes in ICA-transformed Embeddings
https://github.com/ymgw55/WSMD
공식 코드Improving word mover's distance by leveraging self-attention matrix
https://github.com/yogarshi/bisparse-dep
공식 코드Robust Cross-lingual Hypernymy Detection using Dependency Context
https://github.com/yoichi1484/reflection
공식 코드https://github.com/ysu1989/CrossSemparse
공식 코드https://github.com/yuvalpinter/Mimick
공식 코드https://github.com/yuxingch/DialectGram
공식 코드DialectGram: Detecting Dialectal Variation at Multiple Geographic Resolutions
https://github.com/yyaghoobzadeh/WIKI-PSE
공식 코드Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings
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