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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 3876–3900 of 4002 papers

TitleStatusHype
Unveiling the Dreams of Word Embeddings: Towards Language-Driven Image Generation—0
WordRank: Learning Word Embeddings via Robust RankingCode0
Modeling Order in Neural Word Embeddings at Scale—0
Relation Extraction: Perspective from Convolutional Neural Networks—0
Dependency Link Embeddings: Continuous Representations of Syntactic Substructures—0
Semantic Information Extraction for Improved Word Embeddings—0
Word Embeddings vs Word Types for Sequence Labeling: the Curious Case of CV Parsing—0
Neural word embeddings with multiplicative feature interactions for tensor-based compositions—0
DeepNL: a Deep Learning NLP pipeline—0
Neural context embeddings for automatic discovery of word senses—0
Distributional Representations of Words for Short Text Classification—0
Learning Distributed Representations for Multilingual Text Sequences—0
A Word-Embedding-based Sense Index for Regular Polysemy Representation—0
A Simple Word Embedding Model for Lexical Substitution—0
Short Text Clustering via Convolutional Neural NetworksCode1
INESC-ID: A Regression Model for Large Scale Twitter Sentiment Lexicon Induction—0
AZMAT: Sentence Similarity Using Associative Matrices—0
USAAR-WLV: Hypernym Generation with Deep Neural Nets—0
INESC-ID: Sentiment Analysis without Hand-Coded Features or Linguistic Resources using Embedding Subspaces—0
ExB Themis: Extensive Feature Extraction from Word Alignments for Semantic Textual Similarity—0
WarwickDCS: From Phrase-Based to Target-Specific Sentiment Recognition—0
DCU: Using Distributional Semantics and Domain Adaptation for the Semantic Textual Similarity SemEval-2015 Task 2—0
UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification—0
ECNU: Leveraging Word Embeddings to Boost Performance for Paraphrase in Twitter—0
Dependency-Based Semantic Role Labeling using Convolutional Neural Networks—0
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