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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 1451–1460 of 4002 papers

TitleStatusHype
A Strong Baseline for Learning Cross-Lingual Word Embeddings from Sentence Alignments—0
Compound Embedding Features for Semi-supervised Learning—0
Compositional Morpheme Embeddings with Affixes as Functions and Stems as Arguments—0
Astro-HEP-BERT: A bidirectional language model for studying the meanings of concepts in astrophysics and high energy physics—0
A Multi-task Approach to Learning Multilingual Representations—0
A Deep Learning Approach to Behavior-Based Learner Modeling—0
Compositional Fusion of Signals in Data Embedding—0
Associating Neural Word Embeddings With Deep Image Representations Using Fisher Vectors—0
Compositional and Lexical Semantics in RoBERTa, BERT and DistilBERT: A Case Study on CoQA—0
Composing Word Vectors for Japanese Compound Words Using Bilingual Word Embeddings—0
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