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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/Alleansa/eluvio
https://github.com/AmanDaVinci/Universal-Sentence-Representations
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/AndrewJGaut/Towards-Understanding-Gender-Bias-in-Neural-Relation-Extraction
https://github.com/Ardavans/sHDP
https://github.com/Arikskigin/Lda2vec_Implementation
Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec
https://github.com/AryaKoureshi/Magnocellular-Parvocellular-Coactivation-Task-in-MATLAB
https://github.com/Babylonpartners/MultilingualFactorAnalysis
https://github.com/Babylonpartners/fastText_multilingual
https://github.com/BedirYilmaz/picturate-mwml
https://github.com/BigRedT/vico
https://github.com/BramVanroy/astred
Word Alignment by Fine-tuning Embeddings on Parallel Corpora
https://github.com/DW-yejing/fasttext4j-jdk6
https://github.com/DorinK/Implementing-an-SNLI-Paper
Shortcut-Stacked Sentence Encoders for Multi-Domain Inference
https://github.com/ESBigeard/paper_graph
BioSentVec: creating sentence embeddings for biomedical texts
https://github.com/EggplantElf/sclem2017-tagger
https://github.com/ElMehdiBouamama/MBTI-Tweetouilles
https://github.com/EmjayAhn/SuperTML-pytorch
SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data
https://github.com/FID-Biodiversity/GermanWordEmbeddings-NER
Resource-Size matters: Improving Neural Named Entity Recognition with Optimized Large Corpora
https://github.com/FengJiaChunFromSYSU/fastText
https://github.com/FengJiaChunFromSYSU/ntua-slp-semeval2018
NTUA-SLP at SemEval-2018 Task 2: Predicting Emojis using RNNs with Context-aware Attention
https://github.com/GallupGovt/multivac
Domain Adapted Word Embeddings for Improved Sentiment Classification
https://github.com/GeorgeLuImmortal/Adaptive-Tuning-Active-Learning
https://github.com/GeorgeLuImmortal/Effectiveness-of-Pretrained-Transformer-based-Language-Models-in-Active-Learning-for-Labelling-Data
https://github.com/GeorgeLuImmortal/Hierarchical-BERT-Model-with-Limited-Labelled-Data
https://github.com/GilesStrong/SuperTML_HiggsML_Test
SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data
https://github.com/GitHubSprint/fasttext4j
https://github.com/HareeshBahuleyan/size-fit-net
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/Holldean/BERT-Pruning
https://github.com/IBM/wasserstein-barycenters
https://github.com/InsaneLife/ChineseNLPCorpus
https://github.com/JakubStefko/w2vf
https://github.com/Jessicak016/lstm_nli
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/JuliaLiu1997/CISC867
https://github.com/JunnYu/x-transformers-paddle
Transformers without Tears: Improving the Normalization of Self-Attention
https://github.com/KhenAharon/Deep-Learning-SNLI-Residual-Stacked-Encoders
Shortcut-Stacked Sentence Encoders for Multi-Domain Inference
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