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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 901–910 of 4002 papers

TitleStatusHype
Deep Learning Models in Detection of Dietary Supplement Adverse Event Signals from Twitter—0
WG4Rec: Modeling Textual Content with Word Graph for News RecommendationCode0
An Improved Single Step Non-autoregressive Transformer for Automatic Speech Recognition—0
Improving Entity Linking through Semantic Reinforced Entity EmbeddingsCode1
Do Acoustic Word Embeddings Capture Phonological Similarity? An Empirical StudyCode0
Semantic Representation and Inference for NLP—0
Deriving Word Vectors from Contextualized Language Models using Topic-Aware Mention SelectionCode0
PairConnect: A Compute-Efficient MLP Alternative to Attention—0
Shape of Elephant: Study of Macro Properties of Word Embeddings Spaces—0
Predicting the Ordering of Characters in Japanese Historical Documents—0
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