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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 1301–1310 of 4002 papers

TitleStatusHype
Detecting weak and strong Islamophobic hate speech on social media—0
Embodying Pre-Trained Word Embeddings Through Robot Actions—0
Embracing Non-Traditional Linguistic Resources for Low-resource Language Name Tagging—0
Emerging Cross-lingual Structure in Pretrained Language Models—0
EmoDet at SemEval-2019 Task 3: Emotion Detection in Text using Deep Learning—0
Building Semantic Grams of Human Knowledge—0
EMOMINER at SemEval-2019 Task 3: A Stacked BiLSTM Architecture for Contextual Emotion Detection in Text—0
EmoNLP at IEST 2018: An Ensemble of Deep Learning Models and Gradient Boosting Regression Tree for Implicit Emotion Prediction in Tweets—0
Applying Multi-Sense Embeddings for German Verbs to Determine Semantic Relatedness and to Detect Non-Literal Language—0
Better Word Representations with Recursive Neural Networks for Morphology—0
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