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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 1801–1810 of 4002 papers

TitleStatusHype
Argument from Old Man’s View: Assessing Social Bias in Argumentation—0
Implicit Subjective and Sentimental Usages in Multi-sense Word Embeddings—0
A Margin-based Loss with Synthetic Negative Samples for Continuous-output Machine Translation—0
Exploiting Common Characters in Chinese and Japanese to Learn Cross-Lingual Word Embeddings via Matrix Factorization—0
Clustering is Efficient for Approximate Maximum Inner Product Search—0
Improved and Robust Controversy Detection in General Web Pages Using Semantic Approaches under Large Scale Conditions—0
Improved Answer Selection with Pre-Trained Word Embeddings—0
Exploiting Class Labels to Boost Performance on Embedding-based Text Classification—0
Improved CCG Parsing with Semi-supervised Supertagging—0
Clustering Comparable Corpora of Russian and Ukrainian Academic Texts: Word Embeddings and Semantic Fingerprints—0
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