Word Embeddings
1108 papers with code • 0 benchmarks • 52 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Word Embeddings
Datasets
ConceptNet
FrameNet
BookCorpus
WiC
BioASQ
WinoBias
PadChest
WikiMatrix
Subtasks
Latest papers with no code
Improving Acoustic Word Embeddings through Correspondence Training of Self-supervised Speech Representations
HuBERT-based CAE model achieves the best results for word discrimination in all languages, despite Hu-BERT being pre-trained on English only.
Identifying and interpreting non-aligned human conceptual representations using language modeling
In applying this method, we show that congenital blindness induces conceptual reorganization in both a-modal and sensory-related verbal domains, and we identify the associated semantic shifts.
VNLP: Turkish NLP Package
In this work, we present VNLP: the first dedicated, complete, open-source, well-documented, lightweight, production-ready, state-of-the-art Natural Language Processing (NLP) package for the Turkish language.
Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis
To address this, we propose the Information Bottleneck-based Gradient (\texttt{IBG}) explanation framework for ABSA.
Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources
Supervised models for Word Sense Disambiguation (WSD) currently yield to state-of-the-art results in the most popular benchmarks.
Ontology Enhanced Claim Detection
We fused ontology embeddings from a knowledge base with BERT sentence embeddings to perform claim detection for the ClaimBuster and the NewsClaims datasets.
From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings
Embeddings play a pivotal role in the efficacy of Large Language Models.
Word Embeddings Revisited: Do LLMs Offer Something New?
Learning meaningful word embeddings is key to training a robust language model.
Injecting Wiktionary to improve token-level contextual representations using contrastive learning
We also propose two new WiC test sets for which we show that our fine-tuning method achieves substantial improvements.
Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts
We tested our approach on a dataset of 2, 000 tweets about eating disorders, finding that merging word embeddings with knowledge graph information enhances the predictive models' reliability.
