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
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.
Estimating Text Similarity based on Semantic Concept Embeddings
Due to their ease of use and high accuracy, Word2Vec (W2V) word embeddings enjoy great success in the semantic representation of words, sentences, and whole documents as well as for semantic similarity estimation.
MoSECroT: Model Stitching with Static Word Embeddings for Crosslingual Zero-shot Transfer
In this paper, we introduce MoSECroT Model Stitching with Static Word Embeddings for Crosslingual Zero-shot Transfer), a novel and challenging task that is especially relevant to low-resource languages for which static word embeddings are available.
An Analysis of Embedding Layers and Similarity Scores using Siamese Neural Networks
Using medical data, we have analyzed similarity scores of each embedding layer, observing differences in performance among each algorithm.
Effect of dimensionality change on the bias of word embeddings
First, there is a significant variation in the bias of word embeddings with the dimensionality change.
Zur Darstellung eines mehrstufigen Prototypbegriffs in der multilingualen automatischen Sprachgenerierung: vom Korpus über word embeddings bis hin zum automatischen Wörterbuch
The multilingual dictionary of noun valency Portlex is considered to be the trigger for the creation of the automatic language generators Xera and Combinatoria, whose development and use is presented in this paper.
Multi-level biomedical NER through multi-granularity embeddings and enhanced labeling
These results illustrate the proficiency of our proposed model in performing biomedical Named Entity Recognition.
Diffusion-EXR: Controllable Review Generation for Explainable Recommendation via Diffusion Models
Denoising Diffusion Probabilistic Model (DDPM) has shown great competence in image and audio generation tasks.
Multi-Modal Cognitive Maps based on Neural Networks trained on Successor Representations
Cognitive maps, as represented by the entorhinal-hippocampal complex in the brain, organize and retrieve context from memories, suggesting that large language models (LLMs) like ChatGPT could harness similar architectures to function as a high-level processing center, akin to how the hippocampus operates within the cortex hierarchy.
Disentangling continuous and discrete linguistic signals in transformer-based sentence embeddings
We explore whether we can compress transformer-based sentence embeddings into a representation that separates different linguistic signals -- in particular, information relevant to subject-verb agreement and verb alternations.
