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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 3771–3780 of 4002 papers

TitleStatusHype
En-Ar Bilingual Word Embeddings without Word Alignment: Factors Effects—0
Encoders Help You Disambiguate Word Senses in Neural Machine Translation—0
Encoding Prior Knowledge with Eigenword Embeddings—0
Encoding Sentiment Information into Word Vectors for Sentiment Analysis—0
End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings—0
ENGLAWI: From Human- to Machine-Readable Wiktionary—0
English-Malay Cross-Lingual Embedding Alignment using Bilingual Lexicon Augmentation—0
English-Malay Word Embeddings Alignment for Cross-lingual Emotion Classification with Hierarchical Attention Network—0
English Resource Semantics—0
English WordNet Random Walk Pseudo-Corpora—0
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