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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 1601–1610 of 4002 papers

TitleStatusHype
Frequency-based Distortions in Contextualized Word Embeddings—0
Extracting Social Networks from Literary Text with Word Embedding Tools—0
A Semi-universal Pipelined Approach to the CoNLL 2017 UD Shared Task—0
From communities to interpretable network and word embedding: an unified approach—0
A Model of Zero-Shot Learning of Spoken Language Understanding—0
From Image to Text Classification: A Novel Approach based on Clustering Word Embeddings—0
Extracting Possessions from Social Media: Images Complement Language—0
From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli?—0
From meaning to perception -- exploring the space between word and odor perception embeddings—0
Extracting domain-specific terms using contextual word embeddings—0
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