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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 1101–1110 of 4002 papers

TitleStatusHype
Gender bias in (non)-contextual clinical word embeddings for stereotypical medical categories—0
Angular-Based Word Meta-Embedding Learning—0
BhamNLP at SemEval-2020 Task 12: An Ensemble of Different Word Embeddings and Emotion Transfer Learning for Arabic Offensive Language Identification in Social Media—0
Beyond Word Embeddings: Learning Entity and Concept Representations from Large Scale Knowledge Bases—0
An Exploratory Study on Utilising the Web of Linked Data for Product Data Mining—0
A Comprehensive Survey on Word Representation Models: From Classical to State-Of-The-Art Word Representation Language Models—0
Discovering linguistic (ir)regularities in word embeddings through max-margin separating hyperplanes—0
An Exploratory Study on Temporally Evolving Discussion around Covid-19 using Diachronic Word Embeddings—0
Discovering Stylistic Variations in Distributional Vector Space Models via Lexical Paraphrases—0
A General Framework for Detecting Metaphorical Collocations—0
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