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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 1001–1010 of 4002 papers

TitleStatusHype
Eliciting Explicit Knowledge From Domain Experts in Direct Intrinsic Evaluation of Word Embeddings for Specialized DomainsCode0
Country-level Arabic Dialect Identification Using Small Datasets with Integrated Machine Learning Techniques and Deep Learning Models—0
Multi-task Learning Using a Combination of Contextualised and Static Word Embeddings for Arabic Sarcasm Detection and Sentiment Analysis—0
Using contextual and cross-lingual word embeddings to improve variety in template-based NLG for automated journalism—0
Cross-Lingual Transfer Learning for Hate Speech Detection—0
Handling Out-Of-Vocabulary Problem in Hangeul Word Embeddings—0
RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding—0
The Chinese Remainder Theorem for Compact, Task-Precise, Efficient and Secure Word Embeddings—0
Exploiting Position and Contextual Word Embeddings for Keyphrase Extraction from Scientific Papers—0
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa - A Large Romanian Sentiment Data Set—0
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