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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 1411–1420 of 4002 papers

TitleStatusHype
Contextual and Non-Contextual Word Embeddings: an in-depth Linguistic Investigation—0
Metaphor Detection Using Contextual Word Embeddings From Transformers—0
Visual Question Generation from Radiology ImagesCode1
Evaluating Natural Alpha Embeddings on Intrinsic and Extrinsic Tasks—0
Character aware models with similarity learning for metaphor detection—0
Neural Metaphor Detection with a Residual biLSTM-CRF Model—0
Token Level Identification of Multiword Expressions Using Contextual Information—0
Getting the \#\#life out of living: How Adequate Are Word-Pieces for Modelling Complex Morphology?—0
LIT Team's System Description for Japanese-Chinese Machine Translation Task in IWSLT 2020—0
Word Embeddings as Tuples of Feature Probabilities—0
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