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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 1076–1100 of 4002 papers

TitleStatusHype
Leveraging English Word Embeddings for Semi-Automatic Semantic Classification in Nêhiyawêwin (Plains Cree)—0
Morphology-Aware Meta-Embeddings for TamilCode0
Non-Complementarity of Information in Word-Embedding and Brain Representations in Distinguishing between Concrete and Abstract Words—0
Word Embeddings, Cosine Similarity and Deep Learning for Identification of Professions & Occupations in Health-related Social Media—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
CogNLP-Sheffield at CMCL 2021 Shared Task: Blending Cognitively Inspired Features with Transformer-based Language Models for Predicting Eye Tracking Patterns—0
The Effect of Pretraining on Extractive Summarization for Scientific Documents—0
NARNIA at NLP4IF-2021: Identification of Misinformation in COVID-19 Tweets Using BERTweet—0
Profiling of Intertextuality in Latin Literature Using Word EmbeddingsCode0
Exploration des relations sémantiques sous-jacentes aux plongements contextuels de mots (Exploring semantic relations underlying contextual word embeddings)—0
Fine-tuning BERT to classify COVID19 tweets containing symptoms—0
Gender Bias Hidden Behind Chinese Word Embeddings: The Case of Chinese Adjectives—0
Measuring Biases of Word Embeddings: What Similarity Measures and Descriptive Statistics to Use?—0
Experiments on a Guarani Corpus of News and Social Media—0
An Explanatory Query-Based Framework for Exploring Academic Expertise—0
Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering—0
Inspecting the concept knowledge graph encoded by modern language models—0
RAW-C: Relatedness of Ambiguous Words--in Context (A New Lexical Resource for English)Code0
Word Embedding Transformation for Robust Unsupervised Bilingual Lexicon Induction—0
A data-driven strategy to combine word embeddings in information retrieval—0
ViBERTgrid: A Jointly Trained Multi-Modal 2D Document Representation for Key Information Extraction from Documents—0
HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features—0
View Distillation with Unlabeled Data for Extracting Adverse Drug Effects from User-Generated Data—0
Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Media—0
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