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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 3331–3340 of 4002 papers

TitleStatusHype
Character and Subword-Based Word Representation for Neural Language Modeling Prediction—0
Character aware models with similarity learning for metaphor detection—0
Character-Aware Neural Morphological Disambiguation—0
Character-based Neural Machine Translation—0
Character-based Neural Machine Translation—0
Character n-gram Embeddings to Improve RNN Language Models—0
ChatGPT-EDSS: Empathetic Dialogue Speech Synthesis Trained from ChatGPT-derived Context Word Embeddings—0
Chemical Identification and Indexing in PubMed Articles via BERT and Text-to-Text Approaches—0
Chinese Embedding via Stroke and Glyph Information: A Dual-channel View—0
Chinese Event Extraction Using DeepNeural Network with Word Embedding—0
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