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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 2926–2950 of 4002 papers

TitleStatusHype
Monolingual Embeddings for Low Resourced Neural Machine TranslationCode0
Leveraging Linguistic Resources for Improving Neural Text Classification—0
Context Selection for Embedding ModelsCode0
SentiNLP at IJCNLP-2017 Task 4: Customer Feedback Analysis Using a Bi-LSTM-CNN Model—0
YNU-HPCC at IJCNLP-2017 Task 5: Multi-choice Question Answering in Exams Using an Attention-based LSTM Model—0
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
Intelligent Word Embeddings of Free-Text Radiology ReportsCode0
Acquiring Common Sense Spatial Knowledge through Implicit Spatial TemplatesCode0
Unsupervised Morphological Expansion of Small Datasets for Improving Word Embeddings—0
An Unsupervised Approach for Mapping between Vector Spaces—0
Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings—0
Convolutional Neural Network with Word Embeddings for Chinese Word SegmentationCode0
Bayesian Paragraph Vectors—0
Breaking the Softmax Bottleneck: A High-Rank RNN Language ModelCode0
Learning Multi-Modal Word Representation Grounded in Visual Context—0
The Lifted Matrix-Space Model for Semantic CompositionCode0
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