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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 821–830 of 4002 papers

TitleStatusHype
Siamese Networks for Inference in Malayalam Language Texts—0
Multilingual Learning for Mild Cognitive Impairment Screening from a Clinical Speech Task—0
Apples to Apples: A Systematic Evaluation of Topic ModelsCode1
Event Prominence Extraction Combining a Knowledge-Based Syntactic Parser and a BERT Classifier for Dutch—0
The Impact of Word Embeddings on Neural Dependency Parsing—0
Comparing Contextual and Static Word Embeddings with Small Data—0
Position Masking for Improved Layout-Aware Document Understanding—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
Train Short, Test Long: Attention with Linear Biases Enables Input Length ExtrapolationCode2
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