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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 26–50 of 4002 papers

TitleStatusHype
Revisiting Word Embeddings in the LLM Era—0
From Small to Large Language Models: Revisiting the Federalist PapersCode0
Extracting domain-specific terms using contextual word embeddings—0
An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets—0
Rumor Detection by Multi-task Suffix Learning based on Time-series Dual Sentiments—0
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
A Methodology for Studying Linguistic and Cultural Change in China, 1900-1950—0
How does a Multilingual LM Handle Multiple Languages?—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
QuanTaxo: A Quantum Approach to Self-Supervised Taxonomy ExpansionCode0
EmoTech: A Multi-modal Speech Emotion Recognition Using Multi-source Low-level Information with Hybrid Recurrent Network—0
Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models—0
A Multi-tiered Solution for Personalized Baggage Item Recommendations using FastText and Association Rule Mining—0
Analyzing Continuous Semantic Shifts with Diachronic Word Similarity MatricesCode0
Integrating Pause Information with Word Embeddings in Language Models for Alzheimer's Disease Detection from Spontaneous Speech—0
Signatures of prediction during natural listening in MEG data?—0
VITRO: Vocabulary Inversion for Time-series Representation Optimization—0
EF-Net: A Deep Learning Approach Combining Word Embeddings and Feature Fusion for Patient Disposition AnalysisCode0
Learning Complex Word Embeddings in Classical and Quantum Spaces—0
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