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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 2501–2510 of 4002 papers

TitleStatusHype
Inducing Bilingual Lexica From Non-Parallel Data With Earth Mover's Distance Regularization—0
Inducing Distant Supervision in Suggestion Mining through Part-of-Speech Embeddings—0
Inducing Embeddings for Rare and Unseen Words by Leveraging Lexical Resources—0
Inducing Relational Knowledge from BERT—0
INESC-ID: A Regression Model for Large Scale Twitter Sentiment Lexicon Induction—0
INESC-ID at SemEval-2016 Task 4-A: Reducing the Problem of Out-of-Embedding Words—0
INESC-ID: Sentiment Analysis without Hand-Coded Features or Linguistic Resources using Embedding Subspaces—0
InferLite: Simple Universal Sentence Representations from Natural Language Inference Data—0
Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention—0
INF-HatEval at SemEval-2019 Task 5: Convolutional Neural Networks for Hate Speech Detection Against Women and Immigrants on Twitter—0
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