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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 3126–3150 of 4002 papers

TitleStatusHype
Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts—0
Data Sets: Word Embeddings Learned from Tweets and General Data—0
Sentiment Analysis by Joint Learning of Word Embeddings and Classifier—0
Style2Vec: Representation Learning for Fashion Items from Style SetsCode0
Improved Answer Selection with Pre-Trained Word Embeddings—0
Semi-supervised emotion lexicon expansion with label propagation and specialized word embeddingsCode0
Simple and Effective Dimensionality Reduction for Word EmbeddingsCode0
Making Sense of Word EmbeddingsCode0
Identifying Reference Spans: Topic Modeling and Word Embeddings help IR—0
Shortcut-Stacked Sentence Encoders for Multi-Domain InferenceCode0
A Syllable-based Technique for Word Embeddings of Korean Words—0
TopicThunder at SemEval-2017 Task 4: Sentiment Classification Using a Convolutional Neural Network with Distant Supervision—0
NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: a Simple Ensemble Method with Different Embeddings—0
OPI-JSA at SemEval-2017 Task 1: Application of Ensemble learning for computing semantic textual similarity—0
Detecting Anxiety through RedditCode0
HumorHawk at SemEval-2017 Task 6: Mixing Meaning and Sound for Humor Recognition—0
LIA at SemEval-2017 Task 4: An Ensemble of Neural Networks for Sentiment Classification—0
An Artificial Language Evaluation of Distributional Semantic Models—0
deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter—0
Evaluating Feature Extraction Methods for Knowledge-based Biomedical Word Sense Disambiguation—0
Tracing armed conflicts with diachronic word embedding models—0
ELiRF-UPV at SemEval-2017 Task 4: Sentiment Analysis using Deep Learning—0
SimBow at SemEval-2017 Task 3: Soft-Cosine Semantic Similarity between Questions for Community Question Answering—0
DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison—0
DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment AnalysisCode0
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