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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 3021–3030 of 4002 papers

TitleStatusHype
Affective Neural Response Generation—0
StarSpace: Embed All The Things!Code0
Social Media Text Processing and Semantic Analysis for Smart Cities—0
Improving average ranking precision in user searches for biomedical research datasets—0
Semi-Supervised Instance Population of an Ontology using Word Vector Embeddings—0
A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a DiscourseCode0
Using k-way Co-occurrences for Learning Word Embeddings—0
Language Modeling by Clustering with Word Embeddings for Text Readability Assessment—0
Hypothesis Testing based Intrinsic Evaluation of Word Embeddings—0
Learning Neural Word Salience ScoresCode0
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