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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 921–930 of 4002 papers

TitleStatusHype
Leveraging English Word Embeddings for Semi-Automatic Semantic Classification in Nêhiyawêwin (Plains Cree)—0
Non-Complementarity of Information in Word-Embedding and Brain Representations in Distinguishing between Concrete and Abstract Words—0
CogNLP-Sheffield at CMCL 2021 Shared Task: Blending Cognitively Inspired Features with Transformer-based Language Models for Predicting Eye Tracking Patterns—0
Sentence Complexity in Context—0
NARNIA at NLP4IF-2021: Identification of Misinformation in COVID-19 Tweets Using BERTweet—0
Assessing multiple word embeddings for named entity recognition of professions and occupations in health-related social media—0
Lasige-BioTM at ProfNER: BiLSTM-CRF and contextual Spanish embeddings for Named Entity Recognition and Tweet Binary Classification—0
Fine-tuning BERT to classify COVID19 tweets containing symptoms—0
Word Embeddings, Cosine Similarity and Deep Learning for Identification of Professions & Occupations in Health-related Social Media—0
Exploration des relations sémantiques sous-jacentes aux plongements contextuels de mots (Exploring semantic relations underlying contextual word embeddings)—0
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