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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 3101–3110 of 4002 papers

TitleStatusHype
Dual Tensor Model for Detecting Asymmetric Lexico-Semantic Relations—0
Knowledge Distillation for Bilingual Dictionary Induction—0
Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings—0
A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC—0
Ngram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics—0
Exploring Vector Spaces for Semantic Relations—0
Word Embeddings based on Fixed-Size Ordinally Forgetting Encoding—0
AutoExtend: Combining Word Embeddings with Semantic Resources—0
Transfer Learning across Low-Resource, Related Languages for Neural Machine Translation—0
An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing—0
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