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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 981–990 of 4002 papers

TitleStatusHype
A Deep Learning System for Automatic Extraction of Typological Linguistic Information from Descriptive Grammars—0
Position Masking for Improved Layout-Aware Document Understanding—0
Comparing Contextual and Static Word Embeddings with Small Data—0
The Impact of Word Embeddings on Neural Dependency Parsing—0
Word Discriminations for Vocabulary Inventory PredictionCode0
Abstractive Document Summarization with Word Embedding Reconstruction—0
Bilingual Terminology Extraction Using Neural Word Embeddings on Comparable Corpora—0
Siamese Networks for Inference in Malayalam Language Texts—0
Sense representations for Portuguese: experiments with sense embeddings and deep neural language models—0
Effectiveness of Deep Networks in NLP using BiDAF as an example architecture—0
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