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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 1401–1410 of 4002 papers

TitleStatusHype
A Survey On Neural Word Embeddings—0
Context-Aware Neural Machine Translation Decoding—0
context2vec: Learning Generic Context Embedding with Bidirectional LSTM—0
A Survey on Automatically-Constructed WordNets and their Evaluation: Lexical and Word Embedding-based Approaches—0
Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn't.—0
Content Selection through Paraphrase Detection: Capturing different Semantic Realisations of the Same Idea—0
Content-Aware Speaker Embeddings for Speaker Diarisation—0
A Survey Of Cross-lingual Word Embedding Models—0
Constructing Vec-tionaries to Extract Message Features from Texts: A Case Study of Moral Appeals—0
Constructing High Quality Sense-specific Corpus and Word Embedding via Unsupervised Elimination of Pseudo Multi-sense—0
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