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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 31–40 of 4002 papers

TitleStatusHype
Non-Euclidean Hierarchical Representational Learning using Hyperbolic Graph Neural Networks for Environmental Claim Detection—0
Complex Ontology Matching with Large Language Model Embeddings—0
From the New World of Word Embeddings: A Comparative Study of Small-World Lexico-Semantic Networks in LLMs—0
Evolving Hate Speech Online: An Adaptive Framework for Detection and Mitigation—0
Probabilistic Lexical Manifold Construction in Large Language Models via Hierarchical Vector Field Interpolation—0
How does a Multilingual LM Handle Multiple Languages?—0
A Methodology for Studying Linguistic and Cultural Change in China, 1900-1950—0
Comply: Learning Sentences with Complex Weights inspired by Fruit Fly OlfactionCode0
Language Modelling for Speaker Diarization in Telephonic Interviews—0
Document-Level Sentiment Analysis of Urdu Text Using Deep Learning Techniques—0
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