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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 1931–1940 of 4002 papers

TitleStatusHype
Interpretable Neural Embeddings with Sparse Self-Representation—0
A Neural Virtual Anchor Synthesizer based on Seq2Seq and GAN Models—0
Interpretable Word Embedding Contextualization—0
Interpretable Word Embeddings via Informative Priors—0
Interpreting Emoji with Emoji—0
Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings—0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
Interpreting Word-Level Hidden State Behaviour of Character-Level LSTM Language Models—0
A Neural Model for Compositional Word Embeddings and Sentence Processing—0
Dependency Parsing for Urdu: Resources, Conversions and Learning—0
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