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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 641–650 of 4002 papers

TitleStatusHype
Sectioning of Biomedical Abstracts: A Sequence of Sequence Classification Task—0
Modeling Tension in Stories via Commonsense Reasoning and Emotional Word Embeddings—0
Revisiting Additive Compositionality: AND, OR, and NOT Operations with Word Embeddings—0
Impart Contextualization to Static Word Embeddings through Semantic Relations—0
Language Models for Code-switch Detection of te reo Māori and English in a Low-resource Setting—0
Feasibility of BERT Embeddings For Domain-Specific Knowledge Mining—0
Don’t Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings—0
Topic Modeling with Topological Data Analysis—0
Minimally-Supervised Relation Induction from Pre-trained Language Model—0
Cooperative Self-training of Machine Reading Comprehension—0
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