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/shuhengl/acl2023_conllpp
공식 코드https://github.com/shuichiotake/nadp
공식 코드A Neighbourhood-Aware Differential Privacy Mechanism for Static Word Embeddings
https://github.com/silkehusse/re-evaluating-bias
공식 코드Mind Your Bias: A Critical Review of Bias Detection Methods for Contextual Language Models
https://github.com/simone-bombari/attention-sensitivity
공식 코드Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random Features
https://github.com/sjmeis/diffractor
공식 코드https://github.com/skulick/ppchyprep
공식 코드https://github.com/skulick/yiddishtag
공식 코드https://github.com/sola-st/IdBench
공식 코드IdBench: Evaluating Semantic Representations of Identifier Names in Source Code
https://github.com/soltustik/rhg
공식 코드https://github.com/somethingx02/topical_wordvec_models
공식 코드A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings
https://github.com/sonalsannigrahi/document_embeddings
공식 코드Are the Best Multilingual Document Embeddings simply Based on Sentence Embeddings?
https://github.com/soskek/dynamic_neural_text_model
공식 코드https://github.com/speechbrain/speechbrain
공식 코드SoundChoice: Grapheme-to-Phoneme Models with Semantic Disambiguation
https://github.com/spilioeve/Definition-Frames
공식 코드Definition Frames: Using Definitions for Hybrid Concept Representations
https://github.com/stepantita/space-model
공식 코드https://github.com/stephaniebrandl/domain-word-embeddings
공식 코드https://github.com/stevenjamesmoore/aied24
공식 코드https://github.com/strubell/LISA
공식 코드Linguistically-Informed Self-Attention for Semantic Role Labeling
https://github.com/suetAndTie/cycle-image-gan
공식 코드https://github.com/sunipa/On-Measuring-and-Mitigating-Biased-Inferences-of-Word-Embeddings
공식 코드On Measuring and Mitigating Biased Inferences of Word Embeddings
https://github.com/swisscom/churn-intent-DE
공식 코드Churn Intent Detection in Multilingual Chatbot Conversations and Social Media
https://github.com/sz128/few_shot_slot_tagging_and_NER
공식 코드Vector Projection Network for Few-shot Slot Tagging in Natural Language Understanding
https://github.com/takase/alone_seq2seq
공식 코드https://github.com/tanvidadu/Subjective-Bias-Detection
공식 코드Towards Detection of Subjective Bias using Contextualized Word Embeddings
https://github.com/tanyichern/social-biases-contextualized
공식 코드Assessing Social and Intersectional Biases in Contextualized Word Representations
https://github.com/taolusi/chisp
공식 코드https://github.com/taosong2019/utel
공식 코드Knowing Where and What: Unified Word Block Pretraining for Document Understanding
https://github.com/tat-nlp/SART
공식 코드https://github.com/tbmihailov/semeval2016-task3-CQA
공식 코드https://github.com/tca19/near-lossless-binarization
공식 코드https://github.com/tdavislab/verb
공식 코드VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations
https://github.com/tdmeeste/SparseSeqModels
공식 코드https://github.com/tdopierre/FewShotText
공식 코드https://github.com/tensorflow/models
공식 코드Adversarial Training Methods for Semi-Supervised Text Classification
https://github.com/texttechnologylab/SemioGraph
공식 코드The Frankfurt Latin Lexicon: From Morphological Expansion and Word Embeddings to SemioGraphs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.