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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 2741–2750 of 4002 papers

TitleStatusHype
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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