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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 691–700 of 4002 papers

TitleStatusHype
Character-based Neural Machine Translation—0
Arabic aspect sentiment polarity classification using BERT—0
An Unsupervised Approach for Mapping between Vector Spaces—0
Arabic POS Tagging: Don't Abandon Feature Engineering Just Yet—0
Co-learning of Word Representations and Morpheme Representations—0
A Linguistically Informed Convolutional Neural Network—0
Character n-gram Embeddings to Improve RNN Language Models—0
A Rank-Based Similarity Metric for Word Embeddings—0
ChatGPT-EDSS: Empathetic Dialogue Speech Synthesis Trained from ChatGPT-derived Context Word Embeddings—0
Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings—0
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