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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 2121–2130 of 4002 papers

TitleStatusHype
Rotations and Interpretability of Word Embeddings: the Case of the Russian Language—0
RPD: A Distance Function Between Word Embeddings—0
R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Models—0
RS\_GV at SemEval-2021 Task 1: Sense Relative Lexical Complexity Prediction—0
RUFINO at SemEval-2017 Task 2: Cross-lingual lexical similarity by extending PMI and word embeddings systems with a Swadesh's-like list—0
Ruminating Word Representations with Random Noised Masker—0
Ruminating Word Representations with Random Noise Masking—0
Rumor Detection by Multi-task Suffix Learning based on Time-series Dual Sentiments—0
RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian—0
Sampled Image Tagging and Retrieval Methods on User Generated Content—0
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