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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 3041–3050 of 4002 papers

TitleStatusHype
MappSent: a Textual Mapping Approach for Question-to-Question Similarity—0
Detecting Metaphorical Phrases in the Polish Language—0
Gender Prediction for Chinese Social Media Data—0
Word Embeddings for Multi-label Document Classification—0
Word Embeddings as Features for Supervised Coreference Resolution—0
Extracting Tags from Large Raw Texts Using End-to-End Memory Networks—0
Fully Delexicalized Contexts for Syntax-Based Word Embeddings—0
What do we need to know about an unknown word when parsing German—0
Playing with Embeddings : Evaluating embeddings for Robot Language Learning through MUD Games—0
Character and Subword-Based Word Representation for Neural Language Modeling Prediction—0
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