https://github.com/Yushi-Hu/Multilingual-AWE
공식 코드Multilingual Jointly Trained Acoustic and Written Word Embeddings
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/Yushi-Hu/Query-by-Example
공식 코드Acoustic span embeddings for multilingual query-by-example search
https://github.com/a1da4/svp-swap
공식 코드https://github.com/aaaasssddf/PIP-experiments
공식 코드https://github.com/aaaasssddf/global-anchor-method
공식 코드The Global Anchor Method for Quantifying Linguistic Shifts and Domain Adaptation
https://github.com/aametwally/LearningFoodPreferences
공식 코드https://github.com/abefrandsen/syntactic-rand-walk
공식 코드Understanding Composition of Word Embeddings via Tensor Decomposition
https://github.com/acbull/HiCE
공식 코드Few-Shot Representation Learning for Out-Of-Vocabulary Words
https://github.com/adalmia96/Cluster-Analysis
공식 코드Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!
https://github.com/adamlauretig/bwe
공식 코드Identification, Interpretability, and Bayesian Word Embeddings
https://github.com/adamlauretig/bwe_application_naacl_2019
공식 코드Identification, Interpretability, and Bayesian Word Embeddings
https://github.com/adjidieng/DETM
공식 코드https://github.com/adjidieng/ETM
공식 코드https://github.com/adrianakubecova/lime_dp
공식 코드Explaining word embeddings with perfect fidelity: Case study in research impact prediction
https://github.com/adrianchifu/FreSaDa
공식 코드FreSaDa: A French Satire Data Set for Cross-Domain Satire Detection
https://github.com/advaithavetagiri/cnlp-nits-pp
공식 코드https://github.com/afrozas/skip-thought-gan
공식 코드Generating Text through Adversarial Training using Skip-Thought Vectors
https://github.com/aggiejiang/sexwes
공식 코드https://github.com/ahare63/categorical-to-latent
공식 코드https://github.com/ahclab/reflection
공식 코드https://github.com/ai-growth-lab/semp_patent
공식 코드A Survey on Sentence Embedding Models Performance for Patent Analysis
https://github.com/ai-ku/wvec
공식 코드Substitute Based SCODE Word Embeddings in Supervised NLP Tasks
https://github.com/ai4bharat-indicnlp/indicnlp_corpus
공식 코드AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages
https://github.com/ajesujoba/YorubaTwi-Embedding
공식 코드Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yorùbá and Twi
https://github.com/akkikiki/modularity_metric
공식 코드A Resource-Free Evaluation Metric for Cross-Lingual Word Embeddings Based on Graph Modularity
https://github.com/akutuzov/russian_wsi
공식 코드Russian word sense induction by clustering averaged word embeddings
https://github.com/akutuzov/semeval2020
공식 코드UiO-UvA at SemEval-2020 Task 1: Contextualised Embeddings for Lexical Semantic Change Detection
https://github.com/alan-turing-institute/DUKweb
공식 코드DUKweb: Diachronic word representations from the UK Web Archive corpus
https://github.com/albertwy/SWRM
공식 코드Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors
https://github.com/aldrich2y/mianet
공식 코드MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation
https://github.com/alex-tifrea/poincare_glove
공식 코드https://github.com/alexandra-chron/wassa-2018
공식 코드NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion Classification
https://github.com/alexandres/lexvec
공식 코드Incorporating Subword Information into Matrix Factorization Word Embeddings
https://github.com/alexerdmann/ParadigmDiscovery
공식 코드https://github.com/alexeyev/awesome-kyrgyz-nlp
공식 코드https://github.com/alexeyev/kyrgyz-embedding-evaluation
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.