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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 1741–1750 of 4002 papers

TitleStatusHype
How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?—0
Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis—0
Exploring Embeddings for Measuring Text Relatedness: Unveiling Sentiments and Relationships in Online Comments—0
Exploring Distributional Representations and Machine Translation for Aspect-based Cross-lingual Sentiment Classification.—0
CNN- and LSTM-based Claim Classification in Online User Comments—0
How much do word embeddings encode about syntax?—0
Exploring Convolutional Neural Networks for Sentiment Analysis of Spanish tweets—0
Exploring Category Structure with Contextual Language Models and Lexical Semantic Networks—0
Exploring Bilingual Word Embeddings for Hiligaynon, a Low-Resource Language—0
Cluster Labeling by Word Embeddings and WordNet's Hypernymy—0
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