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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 2951–2975 of 4002 papers

TitleStatusHype
Improving the Accuracy of Pre-trained Word Embeddings for Sentiment AnalysisCode0
SPINE: SParse Interpretable Neural EmbeddingsCode0
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
Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings—0
An Unsupervised Approach for Mapping between Vector Spaces—0
Convolutional Neural Network with Word Embeddings for Chinese Word SegmentationCode0
Breaking the Softmax Bottleneck: A High-Rank RNN Language ModelCode0
Bayesian Paragraph Vectors—0
Learning Multi-Modal Word Representation Grounded in Visual Context—0
The Lifted Matrix-Space Model for Semantic CompositionCode0
Evaluation of Croatian Word EmbeddingsCode0
Learning Word Embeddings from Speech—0
Compressing Word Embeddings via Deep Compositional Code LearningCode0
Fine-tuning Tree-LSTM for phrase-level sentiment classification on a Polish dependency treebank. Submission to PolEval task 2Code0
Comparing Recurrent and Convolutional Architectures for English-Hindi Neural Machine Translation—0
Learning Transferable Representation for Bilingual Relation Extraction via Convolutional Neural Networks—0
Embracing Non-Traditional Linguistic Resources for Low-resource Language Name Tagging—0
Semantic Features Based on Word Alignments for Estimating Quality of Text Simplification—0
A Multi-task Learning Approach to Adapting Bilingual Word Embeddings for Cross-lingual Named Entity Recognition—0
Sentence Modeling with Deep Neural Architecture using Lexicon and Character Attention Mechanism for Sentiment Classification—0
A Bag of Useful Tricks for Practical Neural Machine Translation: Embedding Layer Initialization and Large Batch SizeCode0
Event Ordering with a Generalized Model for Sieve Prediction Ranking—0
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