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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 1921–1930 of 4002 papers

TitleStatusHype
Integrating Topic Modeling with Word Embeddings by Mixtures of vMFs—0
Integration of Domain Knowledge using Medical Knowledge Graph Deep Learning for Cancer Phenotyping—0
Automated Scoring of Clinical Expressive Language Evaluation Tasks—0
Automated Preamble Detection in Dictated Medical Reports—0
Interactive Re-Fitting as a Technique for Improving Word Embeddings—0
Cross-lingual Word Sense Disambiguation using mBERT Embeddings with Syntactic Dependencies—0
BERT-Based Neural Collaborative Filtering and Fixed-Length Contiguous Tokens Explanation—0
Interpretable Adversarial Training for Text—0
Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts—0
A Neural Virtual Anchor Synthesizer based on Seq2Seq and GAN Models—0
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