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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 2931–2940 of 4002 papers

TitleStatusHype
LDCCNLP at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases Using Machine Learning—0
All-In-1 at IJCNLP-2017 Task 4: Short Text Classification with One Model for All Languages—0
MainiwayAI at IJCNLP-2017 Task 2: Ensembles of Deep Architectures for Valence-Arousal Prediction—0
Bingo at IJCNLP-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification—0
The Sentimental Value of Chinese Sub-Character Components—0
Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity—0
An Exploration of Word Embedding Initialization in Deep-Learning Tasks—0
SPINE: SParse Interpretable Neural EmbeddingsCode0
Improving the Accuracy of Pre-trained Word Embeddings for Sentiment AnalysisCode0
Word Embeddings Quantify 100 Years of Gender and Ethnic StereotypesCode0
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