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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 151–200 of 4002 papers

TitleStatusHype
Enhancing Scalability of Metric Differential Privacy via Secret Dataset Partitioning and Benders Decomposition—0
DALLMi: Domain Adaption for LLM-based Multi-label ClassifierCode0
1-Diffractor: Efficient and Utility-Preserving Text Obfuscation Leveraging Word-Level Metric Differential PrivacyCode0
CharacterFactory: Sampling Consistent Characters with GANs for Diffusion ModelsCode3
Bridging Vision and Language Spaces with Assignment PredictionCode0
WordDecipher: Enhancing Digital Workspace Communication with Explainable AI for Non-native English Speakers—0
Weakly Supervised Deep Hyperspherical Quantization for Image RetrievalCode0
IITK at SemEval-2024 Task 1: Contrastive Learning and Autoencoders for Semantic Textual Relatedness in Multilingual TextsCode0
BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph FilteringCode0
Robust Concept Erasure Using Task Vectors—0
PejorativITy: Disambiguating Pejorative Epithets to Improve Misogyny Detection in Italian TweetsCode0
Breaking the Silence Detecting and Mitigating Gendered Abuse in Hindi, Tamil, and Indian English Online SpacesCode0
DiLM: Distilling Dataset into Language Model for Text-level Dataset DistillationCode1
The Shape of Word Embeddings: Quantifying Non-Isometry With Topological Data AnalysisCode0
Quantum Natural Language Processing—0
Debiasing Sentence Embedders through Contrastive Word PairsCode0
SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level AttacksCode0
Fusion approaches for emotion recognition from speech using acoustic and text-based features—0
Projective Methods for Mitigating Gender Bias in Pre-trained Language ModelsCode0
Introducing Syllable Tokenization for Low-resource Languages: A Case Study with Swahili—0
Advancing Fake News Detection: Hybrid DeepLearning with FastText and Explainable AI—0
A comparative analysis of embedding models for patent similarity—0
An efficient domain-independent approach for supervised keyphrase extraction and ranking—0
Empowering Segmentation Ability to Multi-modal Large Language ModelsCode0
Leveraging Linguistically Enhanced Embeddings for Open Information Extraction—0
Improving Acoustic Word Embeddings through Correspondence Training of Self-supervised Speech RepresentationsCode0
Identifying and interpreting non-aligned human conceptual representations using language modeling—0
VNLP: Turkish NLP PackageCode2
Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis—0
The Foundational Capabilities of Large Language Models in Predicting Postoperative Risks Using Clinical NotesCode0
Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources—0
A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic ChangeCode0
Ontology Enhanced Claim Detection—0
From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings—0
Word Embeddings Revisited: Do LLMs Offer Something New?—0
Injecting Wiktionary to improve token-level contextual representations using contrastive learning—0
Semi-Supervised Learning for Bilingual Lexicon InductionCode0
Inducing Systematicity in Transformers by Attending to Structurally Quantized EmbeddingsCode1
Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts—0
Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random FeaturesCode0
Layer-Wise Analysis of Self-Supervised Acoustic Word Embeddings: A Study on Speech Emotion Recognition—0
Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene ClassificationCode1
Predicting ATP binding sites in protein sequences using Deep Learning and Natural Language Processing—0
Graph-based Clustering for Detecting Semantic Change Across Time and LanguagesCode0
SWEA: Updating Factual Knowledge in Large Language Models via Subject Word Embedding AlteringCode0
Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMsCode0
Multi-class Regret Detection in Hindi Devanagari Script—0
CERM: Context-aware Literature-based Discovery via Sentiment Analysis—0
Pre-training and Diagnosing Knowledge Base Completion ModelsCode1
Semantic Properties of cosine based bias scores for word embeddingsCode0
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