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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 3551–3600 of 4002 papers

TitleStatusHype
Classification of Medication-Related Tweets Using Stacked Bidirectional LSTMs with Context-Aware AttentionCode0
SemGloVe: Semantic Co-occurrences for GloVe from BERTCode0
Equalizing Gender Biases in Neural Machine Translation with Word Embeddings TechniquesCode0
Learning Personal Food Preferences via Food Logs EmbeddingCode0
CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic ModelingCode0
VCWE: Visual Character-Enhanced Word EmbeddingsCode0
ESTEEM: A Novel Framework for Qualitatively Evaluating and Visualizing Spatiotemporal Embeddings in Social MediaCode0
ESTeR: Combining Word Co-occurrences and Word Associations for Unsupervised Emotion DetectionCode0
Transition-based Neural RST Parsing with Implicit Syntax FeaturesCode0
AnlamVer: Semantic Model Evaluation Dataset for Turkish - Word Similarity and RelatednessCode0
Aligning Word Vectors on Low-Resource Languages with WiktionaryCode0
Text-based depression detection on sparse dataCode0
On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment AnalysisCode0
Learning Representations Specialized in Spatial Knowledge: Leveraging Language and VisionCode0
ETNLP: a visual-aided systematic approach to select pre-trained embeddings for a downstream taskCode0
Parameter-free Sentence Embedding via Orthogonal BasisCode0
Word Embeddings for Entity-annotated TextsCode0
Learning Semantic Representations for Novel Words: Leveraging Both Form and ContextCode0
Learning semantic sentence representations from visually grounded language without lexical knowledgeCode0
Semi-supervised emotion lexicon expansion with label propagation and specialized word embeddingsCode0
Evaluating Biased Attitude Associations of Language Models in an Intersectional ContextCode0
Evaluating Bias In Dutch Word EmbeddingsCode0
Evaluating bilingual word embeddings on the long tailCode0
Vector Embedding of Wikipedia Concepts and EntitiesCode0
Semi-Supervised Learning for Bilingual Lexicon InductionCode0
Ontology-Aware Token Embeddings for Prepositional Phrase AttachmentCode0
Word-Level Loss Extensions for Neural Temporal Relation ClassificationCode0
A Survey on Contextualised Semantic Shift DetectionCode0
Text classification with word embedding regularization and soft similarity measureCode0
Transparent, Efficient, and Robust Word Embedding Access with WOMBATCode0
A Survey of Word Embeddings Evaluation MethodsCode0
An Evaluation Dataset for Legal Word Embedding: A Case Study On Chinese CodexCode0
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual RetrievalCode0
Learning Text Representations for 500K Classification Tasks on Named Entity DisambiguationCode0
Word-level Textual Adversarial Attacking as Combinatorial OptimizationCode0
Evaluating Neural Word Embeddings for SanskritCode0
SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level AttacksCode0
Unsupervised Keyphrase Extraction from Scientific PublicationsCode0
IdBench: Evaluating Semantic Representations of Identifier Names in Source CodeCode0
Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classificationCode0
Evaluating Sparse Interpretable Word Embeddings for Biomedical DomainCode0
Opinions are Made to be Changed: Temporally Adaptive Stance ClassificationCode0
A study of text representations in Hate Speech DetectionCode0
sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word EmbeddingsCode0
Swap and Predict -- Predicting the Semantic Changes in Words across Corpora by Context SwappingCode0
Sense Embeddings are also Biased--Evaluating Social Biases in Static and Contextualised Sense EmbeddingsCode0
Learning to Represent Bilingual DictionariesCode0
Evaluating Unsupervised Dutch Word Embeddings as a Linguistic ResourceCode0
Tree-Stack LSTM in Transition Based Dependency ParsingCode0
A Study of Slang Representation MethodsCode0
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