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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 2851–2860 of 4002 papers

TitleStatusHype
Closed Form Word Embedding Alignment—0
Abstractive Document Summarization with Word Embedding Reconstruction—0
Abstractive Text Summarization: Enhancing Sequence-to-Sequence Models Using Word Sense Disambiguation and Semantic Content Generalization—0
Abusive language in Spanish children and young teenager's conversations: data preparation and short text classification with contextual word embeddings—0
A Call for More Rigor in Unsupervised Cross-lingual Learning—0
A Case Study to Reveal if an Area of Interest has a Trend in Ongoing Tweets Using Word and Sentence Embeddings—0
A* CCG Parsing with a Supertag-factored Model—0
Accurate Dependency Parsing and Tagging of Latin—0
A Challenge Set and Methods for Noun-Verb Ambiguity—0
A Chinese Writing Correction System for Learning Chinese as a Foreign Language—0
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