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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 3051–3060 of 4002 papers

TitleStatusHype
An Introduction to Robust Graph Convolutional Networks—0
An Investigation of the Interactions Between Pre-Trained Word Embeddings, Character Models and POS Tags in Dependency Parsing—0
An Iterative Approach for Unsupervised Most Frequent Sense Detection using WordNet and Word Embeddings—0
An LSTM Approach to Short Text Sentiment Classification with Word Embeddings—0
Annotating Educational Questions for Student Response Analysis—0
A non-DNN Feature Engineering Approach to Dependency Parsing -- FBAML at CoNLL 2017 Shared Task—0
An Ontology-Based Method for Extracting and Classifying Domain-Specific Compositional Nominal Compounds—0
Query Obfuscation Semantic Decomposition—0
A Note on Argumentative Topology: Circularity and Syllogisms as Unsolved Problems—0
A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC—0
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