https://github.com/wbglaeser/brexit-sentiment
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
Papers with Code 아카이브
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/wds2014/wete
Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings
https://github.com/webis-de/small-text
https://github.com/weixsong/bilm-tf
Evaluation of sentence embeddings in downstream and linguistic probing tasks
https://github.com/weiyezhimeng/silent-guardian
https://github.com/wenoptics/viz-of-gender-stereotypes-from-texts
The Cinderella Complex: Word Embeddings Reveal ender Stereotypes in Movies and Books
https://github.com/wesselreijngoud/masterthesis2019
MoNoise: Modeling Noise Using a Modular Normalization System
https://github.com/williamleif/histwords
Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
https://github.com/williamleif/socialsent
Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora
https://github.com/williamscott701/Embedding-LJST
Topic Modeling over Short Texts by Incorporating Word Embeddings
https://github.com/wyfish/fastText
https://github.com/xashru/mixup-text
Augmenting Data with Mixup for Sentence Classification: An Empirical Study
https://github.com/xcfcode/Summarization-Papers
https://github.com/xhan77/AdaptaBERT
Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling
https://github.com/xlang-ai/instructor-embedding
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
https://github.com/xuzhezhaozhao/fastText_reading
https://github.com/yagudinamir/emoji
emoji2vec: Learning Emoji Representations from their Description
https://github.com/yahoojapan/yskip
https://github.com/yangyucheng000/Papers/tree/main/gntm-ck-code
https://github.com/yangyucheng000/University/tree/main/model-1/persimmon
Transformers without Tears: Improving the Normalization of Self-Attention
https://github.com/yangzonglin1994/bilm-tf-extended
Evaluation of sentence embeddings in downstream and linguistic probing tasks
https://github.com/ye-kyaw-thu/preprints
https://github.com/yeranosyanvahan/SuperTML
SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data
https://github.com/yfreedomliTHU/mos-pytorch1.1
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
https://github.com/yifan0sun/DynamicWord2Vec
https://github.com/young-zonglin/bilm-tf-extended
Evaluation of sentence embeddings in downstream and linguistic probing tasks
https://github.com/yugaljain1999/biasfree_bot_GeDi
https://github.com/yukisea/ETM
https://github.com/yunsukim86/wbw-lm
https://github.com/zafercavdar/fasttext-langdetect
https://github.com/zalandoresearch/flair
https://github.com/zhang2010hao/cw2vec-pytorch
https://github.com/zhangyaoyuan/GAN-Simplification
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
https://github.com/zhongpeixiang/ckc
https://github.com/zhou059/w266-project
Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops
https://github.com/ziegler-ingo/cleavage_prediction
Neural Morphological Tagging from Characters for Morphologically Rich Languages
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.