Word Embeddings
1106 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
CELEX
Subtasks
Latest papers with no code
Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random Features
Unveiling the reasons behind the exceptional success of transformers requires a better understanding of why attention layers are suitable for NLP tasks.
Layer-Wise Analysis of Self-Supervised Acoustic Word Embeddings: A Study on Speech Emotion Recognition
Through a comparative experiment and a layer-wise accuracy analysis on two distinct corpora, IEMOCAP and ESD, we explore differences between AWEs and raw self-supervised representations, as well as the proper utilization of AWEs alone and in combination with word embeddings.
Predicting ATP binding sites in protein sequences using Deep Learning and Natural Language Processing
Predicting ATP-Protein Binding sites in genes is of great significance in the field of Biology and Medicine.
Multi-class Regret Detection in Hindi Devanagari Script
We use a pre-trained BERT model to generate word embeddings for the Hindi dataset and also compare deep learning models with conventional machine learning models in order to demonstrate accuracy.
Semantic Properties of cosine based bias scores for word embeddings
Furthermore, we formally analyze cosine based scores from the literature with regard to these requirements.
CERM: Context-aware Literature-based Discovery via Sentiment Analysis
Driven by the abundance of biomedical publications, we introduce a sentiment analysis task to understand food-health relationship.
Expressivity-aware Music Performance Retrieval using Mid-level Perceptual Features and Emotion Word Embeddings
On the text side, we use emotion-enriched word embeddings (EWE) and on the audio side, we extract mid-level perceptual features instead of generic audio embeddings.
Multilingual acoustic word embeddings for zero-resource languages
This research addresses the challenge of developing speech applications for zero-resource languages that lack labelled data.
GWPT: A Green Word-Embedding-based POS Tagger
As a fundamental tool for natural language processing (NLP), the part-of-speech (POS) tagger assigns the POS label to each word in a sentence.
Machine Learning to Promote Translational Research: Predicting Patent and Clinical Trial Inclusion in Dementia Research
Projected to impact 1. 6 million people in the UK by 2040 and costing {\pounds}25 billion annually, dementia presents a growing challenge to society.
