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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 3191–3200 of 4002 papers

TitleStatusHype
Automated Single-Label Patent Classification using Ensemble Classifiers—0
Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers—0
Automated Trustworthiness Testing for Machine Learning Classifiers—0
Automatically Building a Multilingual Lexicon of False Friends With No Supervision—0
Automatically Inferring Implicit Properties in Similes—0
Automatically Linking Lexical Resources with Word Sense Embedding Models—0
Automatic classification of speech overlaps: Feature representation and algorithms—0
Automatic coding of students' writing via Contrastive Representation Learning in the Wasserstein space—0
Automatic Community Creation for Abstractive Spoken Conversations Summarization—0
Automatic Creation of Correspondence Table of Meaning Tags from Two Dictionaries in One Language Using Bilingual Word Embedding—0
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