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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 701–710 of 4002 papers

TitleStatusHype
Looking Into the Black Box - How Are Idioms Processed in BERT?—0
Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge—0
Improving Word Translation via Two-Stage Contrastive LearningCode1
First Bilingual Word Embeddings for te reo Māori and English: Towards Code-switching Detection in a Low-resourced setting—0
Non-Linear Relational Information Probing in Word Embeddings—0
Sentence Selection Strategies for Distilling Word Embeddings from BERT—0
Vec2Node: Self-training with Tensor Augmentation for Text Classification with Few Labels—0
Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to Modeling—0
FeelsGoodMan: Inferring Semantics of Twitch Neologisms—0
Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics—0
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