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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 2101–2110 of 4002 papers

TitleStatusHype
Identification, Interpretability, and Bayesian Word EmbeddingsCode0
SWOW-8500: Word Association task for Intrinsic Evaluation of Word Embeddings—0
Clustering-Based Article Identification in Historical NewspapersCode0
Stance Classification, Outcome Prediction, and Impact Assessment: NLP Tasks for Studying Group Decision-Making—0
Multilingual segmentation based on neural networks and pre-trained word embeddings—0
Suicide Risk Assessment on Social Media: USI-UPF at the CLPsych 2019 Shared Task—0
Deep Learning Techniques for Humor Detection in Hindi-English Code-Mixed Tweets—0
Learning Unsupervised Multilingual Word Embeddings with Incremental Multilingual Hubs—0
THU\_NGN at SemEval-2019 Task 3: Dialog Emotion Classification using Attentional LSTM-CNN—0
A k-Nearest Neighbor Approach towards Multi-level Sequence Labeling—0
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