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Word Embeddings

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.

( Image credit: Dynamic Word Embedding for Evolving Semantic Discovery )

Papers

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Showing 626–650 of 4002 papers

TitleStatusHype
Density Matching for Bilingual Word EmbeddingCode0
Debiasing Sentence Embedders through Contrastive Word PairsCode0
Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across LanguagesCode0
BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 LanguagesCode0
Debiasing Word Embeddings with Nonlinear GeometryCode0
AnlamVer: Semantic Model Evaluation Dataset for Turkish - Word Similarity and RelatednessCode0
Do CoNLL-2003 Named Entity Taggers Still Work Well in 2023?Code0
Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMsCode0
Breaking the Silence Detecting and Mitigating Gendered Abuse in Hindi, Tamil, and Indian English Online SpacesCode0
Breaking the Softmax Bottleneck: A High-Rank RNN Language ModelCode0
Domain-Specific Word Embeddings with Structure PredictionCode0
Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding SpacesCode0
DebIE: A Platform for Implicit and Explicit Debiasing of Word Embedding SpacesCode0
Bridging Vision and Language Spaces with Assignment PredictionCode0
1-Diffractor: Efficient and Utility-Preserving Text Obfuscation Leveraging Word-Level Metric Differential PrivacyCode0
BRUMS at SemEval-2020 Task 3: Contextualised Embeddings for Predicting the (Graded) Effect of Context in Word SimilarityCode0
Dynamic Word Embeddings for Evolving Semantic DiscoveryCode0
Bilingual Lexicon Induction through Unsupervised Machine TranslationCode0
DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment AnalysisCode0
Debiasing Convolutional Neural Networks via Meta OrthogonalizationCode0
Building a Kannada POS Tagger Using Machine Learning and Neural Network ModelsCode0
Aggressive Language Identification Using Word Embeddings and Sentiment FeaturesCode0
Bilingual Learning of Multi-sense Embeddings with Discrete AutoencodersCode0
Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity RecognitionCode0
Debiasing Multilingual Word Embeddings: A Case Study of Three Indian LanguagesCode0
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