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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 351–400 of 4002 papers

TitleStatusHype
Efficient and Flexible Topic Modeling using Pretrained Embeddings and Bag of SentencesCode1
Vision-Language Models Performing Zero-Shot Tasks Exhibit Gender-based Disparities—0
Machine Translation for Accessible Multi-Language Text Analysis—0
Language Embeddings Sometimes Contain Typological GeneralizationsCode0
News and Load: A Quantitative Exploration of Natural Language Processing Applications for Forecasting Day-ahead Electricity System Demand—0
The 2022 n2c2/UW Shared Task on Extracting Social Determinants of HealthCode0
SensePOLAR: Word sense aware interpretability for pre-trained contextual word embeddingsCode0
Online Fake Review Detection Using Supervised Machine Learning And BERT Model—0
Analyzing the Representational Geometry of Acoustic Word Embeddings—0
Supervised Acoustic Embeddings And Their Transferability Across LanguagesCode0
The Undesirable Dependence on Frequency of Gender Bias Metrics Based on Word EmbeddingsCode0
Using meaning instead of words to track topics—0
TegFormer: Topic-to-Essay Generation with Good Topic Coverage and High Text Coherence—0
Text classification in shipping industry using unsupervised models and Transformer based supervised models—0
Norm of Word Embedding Encodes Information Gain—0
Multi hash embeddings in spaCyCode0
Do CoNLL-2003 Named Entity Taggers Still Work Well in 2023?Code0
Exploring Interpretability of Independent Components of Word Embeddings with Automated Word Intruder Test—0
The effects of gender bias in word embeddings on depression prediction—0
AsPOS: Assamese Part of Speech Tagger using Deep Learning Approach—0
ReDDIT: Regret Detection and Domain Identification from Text—0
Explainability of Text Processing and Retrieval Methods: A Critical Survey—0
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of ContextsCode1
RPN: A Word Vector Level Data Augmentation Algorithm in Deep Learning for Language UnderstandingCode0
A Study of Slang Representation MethodsCode0
Spatio-temporal Sign Language Representation and Translation—0
Tracking Semantic Shifts in German Court Decisions with Diachronic Word EmbeddingsCode0
Learning Object-Language Alignments for Open-Vocabulary Object DetectionCode1
Semantic Guided Level-Category Hybrid Prediction Network for Hierarchical Image Classification—0
SexWEs: Domain-Aware Word Embeddings via Cross-lingual Semantic Specialisation for Chinese Sexism Detection in Social MediaCode0
ALIGN-MLM: Word Embedding Alignment is Crucial for Multilingual Pre-trainingCode1
Mind Your Bias: A Critical Review of Bias Detection Methods for Contextual Language ModelsCode0
Investigating the Frequency Distortion of Word Embeddings and Its Impact on Bias MetricsCode0
Improving word mover's distance by leveraging self-attention matrixCode1
ADEPT: A DEbiasing PrompT FrameworkCode1
Combining Contrastive Learning and Knowledge Graph Embeddings to develop medical word embeddings for the Italian language—0
Hyperbolic Centroid Calculations for Text Classification—0
Using Large Pre-Trained Language Model to Assist FDA in Premarket Medical Device—0
Improving Bilingual Lexicon Induction with Cross-Encoder RerankingCode1
Using Context-to-Vector with Graph Retrofitting to Improve Word EmbeddingsCode1
Analyzing Acoustic Word Embeddings from Pre-trained Self-supervised Speech Models—0
Feature Engineering vs BERT on Twitter Data—0
MorphTE: Injecting Morphology in Tensorized EmbeddingsCode1
Leveraging knowledge graphs to update scientific word embeddings using latent semantic imputation—0
Sinhala Sentence Embedding: A Two-Tiered Structure for Low-Resource Languages—0
A Robust Bias Mitigation Procedure Based on the Stereotype Content ModelCode0
On the Curious Case of _2 norm of Sense Embeddings—0
Discovering Differences in the Representation of People using Contextualized Semantic AxesCode1
Fine-mixing: Mitigating Backdoors in Fine-tuned Language ModelsCode8
Eye-tracking based classification of Mandarin Chinese readers with and without dyslexia using neural sequence modelsCode0
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