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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 1071–1080 of 4002 papers

TitleStatusHype
Data Filtering using Cross-Lingual Word Embeddings—0
The Effect of Pretraining on Extractive Summarization for Scientific Documents—0
Morphology-Aware Meta-Embeddings for TamilCode0
NARNIA at NLP4IF-2021: Identification of Misinformation in COVID-19 Tweets Using BERTweet—0
Fine-tuning BERT to classify COVID19 tweets containing symptoms—0
Looking for a Role for Word Embeddings in Eye-Tracking Features Prediction: Does Semantic Similarity Help?—0
Assessing multiple word embeddings for named entity recognition of professions and occupations in health-related social media—0
CogNLP-Sheffield at CMCL 2021 Shared Task: Blending Cognitively Inspired Features with Transformer-based Language Models for Predicting Eye Tracking Patterns—0
Measuring Biases of Word Embeddings: What Similarity Measures and Descriptive Statistics to Use?—0
Field Embedding: A Unified Grain-Based Framework for Word Representation—0
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