Word Embeddings

1096 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 )

Latest papers with no code

Word Embeddings Revisited: Do LLMs Offer Something New?

no code yet • 16 Feb 2024

Learning meaningful word embeddings is key to training a robust language model.

Injecting Wiktionary to improve token-level contextual representations using contrastive learning

no code yet • 12 Feb 2024

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

no code yet • 8 Feb 2024

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.

Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random Features

no code yet • 5 Feb 2024

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

no code yet • 4 Feb 2024

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

no code yet • 2 Feb 2024

Predicting ATP-Protein Binding sites in genes is of great significance in the field of Biology and Medicine.

SWEA: Changing Factual Knowledge in Large Language Models via Subject Word Embedding Altering

no code yet • 31 Jan 2024

To further validate the reasoning ability of SWEA$\oplus$OS in editing knowledge, we evaluate it on the more complex RippleEdits benchmark.

Multi-class Regret Detection in Hindi Devanagari Script

no code yet • 29 Jan 2024

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

no code yet • 27 Jan 2024

Furthermore, we formally analyze cosine based scores from the literature with regard to these requirements.

CERM: Context-aware Literature-based Discovery via Sentiment Analysis

no code yet • 27 Jan 2024

Driven by the abundance of biomedical publications, we introduce a sentiment analysis task to understand food-health relationship.