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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 2891–2900 of 4002 papers

TitleStatusHype
Adapting Topic Models using Lexical Associations with Tree Priors—0
Adapting word2vec to Named Entity Recognition—0
Adaptive Compression of Word Embeddings—0
A data-driven strategy to combine word embeddings in information retrieval—0
Additional Shared Decoder on Siamese Multi-view Encoders for Learning Acoustic Word Embeddings—0
Addressing Biases in the Texts using an End-to-End Pipeline Approach—0
Addressing Low-Resource Scenarios with Character-aware Embeddings—0
Addressing Noise in Multidialectal Word Embeddings—0
Addressing the Challenges of Cross-Lingual Hate Speech Detection—0
A Deep Content-Based Model for Persian Rumor Verification—0
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