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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 3691–3700 of 4002 papers

TitleStatusHype
Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn't.—0
K-Embeddings: Learning Conceptual Embeddings for Words using Context—0
Multimodal Semantic Learning from Child-Directed Input—0
Deconstructing Complex Search Tasks: a Bayesian Nonparametric Approach for Extracting Sub-tasks—0
Dependency Based Embeddings for Sentence Classification Tasks—0
Improved Neural Network-based Multi-label Classification with Better Initialization Leveraging Label Co-occurrence—0
Improve Chinese Word Embeddings by Exploiting Internal StructureCode0
Drop-out Conditional Random Fields for Twitter with Huge Mined Gazetteer—0
Bilingual Word Embeddings from Parallel and Non-parallel Corpora for Cross-Language Text Classification—0
Questioning Arbitrariness in Language: a Data-Driven Study of Conventional Iconicity—0
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