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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 951–960 of 4002 papers

TitleStatusHype
Abstractive Document Summarization with Word Embedding Reconstruction—0
Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing -- A Tale of Two Parsers Revisited—0
Cross-Lingual Word Embeddings and the Structure of the Human Bilingual Lexicon—0
Cross-lingual Word Embeddings beyond Zero-shot Machine Translation—0
Cross-Lingual Word Embeddings for Morphologically Rich Languages—0
Cross-Lingual Word Embeddings for Low-Resource Language Modeling—0
Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach—0
Cross-lingual Word Embeddings in Hyperbolic Space—0
Deep Dialog Act Recognition using Multiple Token, Segment, and Context Information Representations—0
Deep Learning for Opinion Mining and Topic Classification of Course Reviews—0
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