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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
A survey of cross-lingual features for zero-shot cross-lingual semantic parsing—0
Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings—0
A Survey of Active Learning for Text Classification using Deep Neural Networks—0
Consistent Structural Relation Learning for Zero-Shot Segmentation—0
Consistency and Variation in Kernel Neural Ranking Model—0
A supervised approach to taxonomy extraction using word embeddings—0
Analogies Explained: Towards Understanding Word Embeddings—0
A Deep Neural Framework for Contextual Affect Detection—0
A comparative analysis of embedding models for patent similarity—0
Considerations for the Interpretation of Bias Measures of Word Embeddings—0
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