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Word embedding

Known as: Word vector space, Thought vectors, Word vectors 
Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words… Expand
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Related topics

Related topics

18 relations
BioinformaticsBrown clusteringCo-occurrence matrixDeep learning
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2018
Review
2018
The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings
We argue word embedding models are a useful tool for the study of culture using a historical analysis of shared understandings of… Expand
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Review
2017
Review
2017
A Survey Of Cross-lingual Word Embedding Models.
Cross-lingual representations of words enable us to reason about word meaning in multilingual contexts and are a key facilitator… Expand
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Highly Cited
2016
Highly Cited
2016
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with… Expand
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Highly Cited
2015
Highly Cited
2015
From Word Embeddings To Document Distances
We present the Word Mover's Distance (WMD), a novel distance function between text documents. Our work is based on recent results… Expand
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Highly Cited
2015
Highly Cited
2015
Improving Distributional Similarity with Lessons Learned from Word Embeddings
Recent trends suggest that neural-network-inspired word embedding models outperform traditional count-based distributional models… Expand
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Highly Cited
2014
Highly Cited
2014
Neural Word Embedding as Implicit Matrix Factorization
We analyze skip-gram with negative-sampling (SGNS), a word embedding method introduced by Mikolov et al., and show that it is… Expand
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Highly Cited
2014
Highly Cited
2014
Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification
We present a method that learns word embedding for Twitter sentiment classification in this paper. Most existing algorithms for… Expand
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Highly Cited
2014
Highly Cited
2014
Dependency-Based Word Embeddings
While continuous word embeddings are gaining popularity, current models are based solely on linear contexts. In this work, we… Expand
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Highly Cited
2014
Highly Cited
2014
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method
The word2vec software of Tomas Mikolov and colleagues (this https URL ) has gained a lot of traction lately, and provides state… Expand
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Highly Cited
2013
Highly Cited
2013
Efficient Estimation of Word Representations in Vector Space
We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The… Expand
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