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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 1501–1550 of 4002 papers

TitleStatusHype
Neural Metaphor Detection with a Residual biLSTM-CRF Model—0
Evaluating Natural Alpha Embeddings on Intrinsic and Extrinsic Tasks—0
Estimating Mutual Information Between Dense Word Embeddings—0
Character aware models with similarity learning for metaphor detection—0
Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification—0
Improving Biomedical Analogical Retrieval with Embedding of Structural Dependencies—0
Predicting Degrees of Technicality in Automatic Terminology Extraction—0
Token Level Identification of Multiword Expressions Using Contextual Information—0
A Graph-based Coarse-to-fine Method for Unsupervised Bilingual Lexicon Induction—0
Transition-based Semantic Dependency Parsing with Pointer Networks—0
Metaphor Detection Using Contextual Word Embeddings From Transformers—0
Getting the \#\#life out of living: How Adequate Are Word-Pieces for Modelling Complex Morphology?—0
Non-Linear Instance-Based Cross-Lingual Mapping for Non-Isomorphic Embedding Spaces—0
Supervised Understanding of Word Embeddings—0
Using Company Specific Headlines and Convolutional Neural Networks to Predict Stock Fluctuations—0
Dirichlet-Smoothed Word Embeddings for Low-Resource Settings—0
MDR Cluster-Debias: A Nonlinear WordEmbedding Debiasing Pipeline—0
Learning aligned embeddings for semi-supervised word translation using Maximum Mean Discrepancy—0
On the Learnability of Concepts: With Applications to Comparing Word Embedding Algorithms—0
Evaluating a Multi-sense Definition Generation Model for Multiple Languages—0
Attention improves concentration when learning node embeddings—0
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages—0
ScoreGAN: A Fraud Review Detector based on Multi Task Learning of Regulated GAN with Data Augmentation—0
CS-Embed at SemEval-2020 Task 9: The effectiveness of code-switched word embeddings for sentiment analysisCode0
Combining word embeddings and convolutional neural networks to detect duplicated questions—0
ValNorm Quantifies Semantics to Reveal Consistent Valence Biases Across Languages and Over CenturiesCode0
Exploiting Class Labels to Boost Performance on Embedding-based Text Classification—0
Fair Is Better than Sensational: Man Is to Doctor as Woman Is to Doctor—0
\'Etude sur le r\'esum\'e comparatif gr\^ace aux plongements de mots (Comparative summarization study using word embeddings)—0
Unsupervised Word Translation with Adversarial Autoencoder—0
Apprentissage de plongements de mots sur des corpus en langue de sp\'ecialit\'e : une \'etude d'impact (Learning word embeddings on domain specific corpora : an impact study )—0
Du bon usage d'ingr\'edients linguistiques sp\'eciaux pour classer des recettes exceptionnelles (Using Special Linguistic Ingredients to Classify Exceptional Recipes )—0
Hybrid Improved Document-level Embedding (HIDE)—0
R\'epliquer et \'etendre pour l'alsacien ``\'Etiquetage en parties du discours de langues peu dot\'ees par sp\'ecialisation des plongements lexicaux'' (Replicating and extending for Alsatian : ``POS tagging for low-resource languages by adapting word embeddings'')—0
BERT-based Ensembles for Modeling Disclosure and Support in Conversational Social Media Text—0
Word Sense Distance in Human Similarity Judgements and Contextualised Word Embeddings—0
Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings—0
InfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a NonlinearityCode0
Quasi-orthonormal Encoding for Machine Learning ApplicationsCode0
TIME: Text and Image Mutual-Translation Adversarial Networks—0
A Study of Neural Matching Models for Cross-lingual IR—0
AutoSUM: Automating Feature Extraction and Multi-user Preference Simulation for Entity SummarizationCode0
Degree-Aware Alignment for Entities in TailCode0
Acoustic Word Embedding System for Code-Switching Query-by-example Spoken Term Detection—0
Living Machines: A study of atypical animacyCode0
The Frankfurt Latin Lexicon: From Morphological Expansion and Word Embeddings to SemioGraphsCode0
Enhancing Word Embeddings with Knowledge Extracted from Lexical ResourcesCode0
GM-CTSC at SemEval-2020 Task 1: Gaussian Mixtures Cross Temporal Similarity Clustering—0
Embeddings as representation for symbolic music—0
Contextual Embeddings: When Are They Worth It?—0
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