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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 3521–3530 of 4002 papers

TitleStatusHype
Cross-lingual Word Embeddings in Hyperbolic Space—0
Cross-Lingual Word Representations: Induction and Evaluation—0
Cross-lingual Word Sense Disambiguation using mBERT Embeddings with Syntactic Dependencies—0
Crossword: Estimating Unknown Embeddings using Cross Attention and Alignment Strategies—0
CroVeWA: Crosslingual Vector-Based Writing Assistance—0
Cseq2seq: Cyclic Sequence-to-Sequence Learning—0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
CUFE at SemEval-2016 Task 4: A Gated Recurrent Model for Sentiment Classification—0
Cultural Cartography with Word Embeddings—0
CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks—0
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