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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 3276–3300 of 4002 papers

TitleStatusHype
Wordsurf : un outil pour naviguer dans un espace de « Word Embeddings » (Wordsurf : a tool to surf in a ``word embeddings'' space)—0
The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraintsCode0
Deep Learning for Hate Speech Detection in TweetsCode0
Learning to Compute Word Embeddings On the Fly—0
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations—0
Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich LanguagesCode0
The Importance of Automatic Syntactic Features in Vietnamese Named Entity Recognition—0
ASR error management for improving spoken language understanding—0
Second-Order Word Embeddings from Nearest Neighbor Topological FeaturesCode0
Contextualizing Citations for Scientific Summarization using Word Embeddings and Domain Knowledge—0
Lightweight Efficient Multi-keyword Ranked Search over Encrypted Cloud Data using Dual Word Embeddings—0
Learning Semantic Relatedness From Human Feedback Using Metric Learning—0
Mixed Membership Word Embeddings for Computational Social Science—0
Utility of General and Specific Word Embeddings for Classifying Translational Stages of Research—0
Evaluating vector-space models of analogy—0
End-to-end Recurrent Neural Network Models for Vietnamese Named Entity Recognition: Word-level vs. Character-levelCode0
Ontology-Aware Token Embeddings for Prepositional Phrase AttachmentCode0
Supervised Learning of Universal Sentence Representations from Natural Language Inference DataCode1
Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification—0
On the effectiveness of feature set augmentation using clusters of word embeddings—0
Finnish resources for evaluating language model semanticsCode0
Word vectors, reuse, and replicability: Towards a community repository of large-text resources—0
The Making of the Royal Society Corpus—0
Wordnet extension via word embeddings: Experiments on the Norwegian Wordnet—0
Automatic Morpheme Segmentation and Labeling in Universal Dependencies ResourcesCode0
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