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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 2191–2200 of 4002 papers

TitleStatusHype
SenSALDO: Creating a Sentiment Lexicon for Swedish—0
Sense Embeddings are also Biased -- Evaluating Social Biases in Static and Contextualised Sense Embeddings—0
Sense Embeddings are also Biased – Evaluating Social Biases in Static and Contextualised Sense Embeddings—0
SenseFitting: Sense Level Semantic Specialization of Word Embeddings for Word Sense Disambiguation—0
SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysis—0
SensEmbed: Learning Sense Embeddings for Word and Relational Similarity—0
Sense representations for Portuguese: experiments with sense embeddings and deep neural language models—0
Sentence Alignment Methods for Improving Text Simplification Systems—0
Sentence Complexity in Context—0
Sentence Compression by Deletion with LSTMs—0
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