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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 1741–1750 of 4002 papers

TitleStatusHype
How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?—0
Contextual Embeddings: When Are They Worth It?—0
IITK at the FinSim Task: Hypernym Detection in Financial Domain via Context-Free and Contextualized Word Embeddings—0
How much does a word weigh? Weighting word embeddings for word sense induction—0
How Much Does Tokenization Affect Neural Machine Translation?—0
How much do word embeddings encode about syntax?—0
How Robust Are Character-Based Word Embeddings in Tagging and MT Against Wrod Scramlbing or Randdm Nouse?—0
How Self-Attention Improves Rare Class Performance in a Question-Answering Dialogue Agent—0
IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text—0
Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network—0
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