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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 1931–1940 of 4002 papers

TitleStatusHype
ViCo: Word Embeddings from Visual Co-occurrencesCode0
On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning—0
Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing -- A Tale of Two Parsers Revisited—0
Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsing—0
A Neural Virtual Anchor Synthesizer based on Seq2Seq and GAN Models—0
Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding SpacesCode0
Parsimonious Morpheme Segmentation with an Application to Enriching Word Embeddings—0
Scene Classification in Indoor Environments for Robots using Context Based Word Embeddings—0
Understanding Undesirable Word Embedding Associations—0
Language Features Matter: Effective Language Representations for Vision-Language Tasks—0
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