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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 2726–2750 of 4002 papers

TitleStatusHype
Towards Qualitative Word Embeddings Evaluation: Measuring Neighbors Variation—0
attr2vec: Jointly Learning Word and Contextual Attribute Embeddings with Factorization Machines—0
Evaluating bilingual word embeddings on the long tailCode0
T\"ubingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji Prediction—0
Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality—0
NEUROSENT-PDI at SemEval-2018 Task 1: Leveraging a Multi-Domain Sentiment Model for Inferring Polarity in Micro-blog Text—0
EMA at SemEval-2018 Task 1: Emotion Mining for Arabic—0
Igbo Diacritic Restoration using Embedding Models—0
DMCB at SemEval-2018 Task 1: Transfer Learning of Sentiment Classification Using Group LSTM for Emotion Intensity prediction—0
DL Team at SemEval-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and Embeddings—0
The Word Analogy Testing Caveat—0
Discriminating between Lexico-Semantic Relations with the Specialization Tensor ModelCode0
Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings—0
Inducing a Lexicon of Abusive Words – a Feature-Based ApproachCode0
DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning—0
Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings—0
Multimodal Frame Identification with Multilingual Evaluation—0
Context Sensitive Neural Lemmatization with Lematus—0
Pay-Per-Request Deployment of Neural Network Models Using Serverless Architectures—0
Stacking with Auxiliary Features for Visual Question Answering—0
Unsupervised Induction of Linguistic Categories with Records of Reading, Speaking, and Writing—0
Specialising Word Vectors for Lexical EntailmentCode0
Learning Word Embeddings for Data Sparse and Sentiment Rich Data Sets—0
Learning Word Embeddings for Low-Resource Languages by PU Learning—0
Querying Word Embeddings for Similarity and Relatedness—0
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