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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 1011–1020 of 4002 papers

TitleStatusHype
Bingo at IJCNLP-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification—0
Deduplication of Scholarly Documents using Locality Sensitive Hashing and Word Embeddings—0
Deep Bidirectional Transformers for Relation Extraction without Supervision—0
Deep Clustering with Measure Propagation—0
Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing -- A Tale of Two Parsers Revisited—0
Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing - A Tale of Two Parsers Revisited—0
A Comparison of Domain-based Word Polarity Estimation using different Word Embeddings—0
Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts—0
Deep Dialog Act Recognition using Multiple Token, Segment, and Context Information Representations—0
Binary Encoded Word Mover’s Distance—0
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