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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 3051–3060 of 4002 papers

TitleStatusHype
NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets—0
NTUAAILS at SemEval-2020 Task 11: Propaganda Detection and Classification with biLSTMs and ELMo—0
NTU NLP Lab System at SemEval-2018 Task 10: Verifying Semantic Differences by Integrating Distributional Information and Expert Knowledge—0
NUIG at EmoInt-2017: BiLSTM and SVR Ensemble to Detect Emotion Intensity—0
NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity—0
Obtaining referential word meanings from visual and distributional information: Experiments on object naming—0
Offensive Language Detection Using Brown Clustering—0
Offensive Language Detection with BERT-based models, By Customizing Attention Probabilities—0
On Adversarial Examples for Biomedical NLP Tasks—0
On Approximately Searching for Similar Word Embeddings—0
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