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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 1151–1200 of 4002 papers

TitleStatusHype
Do Word Embeddings Capture Spelling Variation?Code0
Discourse Relation Embeddings: Representing the Relations between Discourse Segments in Social MediaCode0
Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor DetectionCode0
Does mBERT understand Romansh? Evaluating word embeddings using word alignmentCode0
Deep Image-to-Recipe TranslationCode0
IMAGINATOR: Pre-Trained Image+Text Joint Embeddings using Word-Level Grounding of ImagesCode0
Domain Adapted Word Embeddings for Improved Sentiment ClassificationCode0
Discovering emergent connections in quantum physics research via dynamic word embeddingsCode0
Improved Relation Extraction with Feature-Rich Compositional Embedding ModelsCode0
Global Textual Relation Embedding for Relational UnderstandingCode0
DeepHateExplainer: Explainable Hate Speech Detection in Under-resourced Bengali LanguageCode0
Improving Acoustic Word Embeddings through Correspondence Training of Self-supervised Speech RepresentationsCode0
Discriminating between Lexico-Semantic Relations with the Specialization Tensor ModelCode0
Improving Chemical Named Entity Recognition in Patents with Contextualized Word EmbeddingsCode0
Document Embedding with Paragraph VectorsCode0
Domain-Specific Word Embeddings with Structure PredictionCode0
Efficient, Compositional, Order-sensitive n-gram EmbeddingsCode0
Empower Sequence Labeling with Task-Aware Neural Language ModelCode0
Aggressive Language Identification Using Word Embeddings and Sentiment FeaturesCode0
Improving Relation Extraction through Syntax-induced Pre-training with Dependency MaskingCode0
Identification, Interpretability, and Bayesian Word EmbeddingsCode0
Living Machines: A study of atypical animacyCode0
Probing for Semantic Classes: Diagnosing the Meaning Content of Word EmbeddingsCode0
Deeper Attention to Abusive User Content Moderation—0
Deep Dialog Act Recognition using Multiple Token, Segment, and Context Information Representations—0
AWE: Asymmetric Word Embedding for Textual Entailment—0
An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing—0
Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts—0
Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing - A Tale of Two Parsers Revisited—0
Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing -- A Tale of Two Parsers Revisited—0
A Dynamic Window Neural Network for CCG Supertagging—0
Deep Clustering with Measure Propagation—0
Deep Bidirectional Transformers for Relation Extraction without Supervision—0
Automating Idea Unit Segmentation and Alignment for Assessing Reading Comprehension via Summary Protocol Analysis—0
Deduplication of Scholarly Documents using Locality Sensitive Hashing and Word Embeddings—0
Automatic Word Association Norms (AWAN)—0
An Empirical Analysis of NMT-Derived Interlingual Embeddings and their Use in Parallel Sentence Identification—0
Deconstructing Word Embeddings—0
Deconstructing word embedding algorithms—0
Automatic Triage of Mental Health Forum Posts—0
Deconstructing Complex Search Tasks: a Bayesian Nonparametric Approach for Extracting Sub-tasks—0
Deconstructing and reconstructing word embedding algorithms—0
Automatic Transformation of Clinical Narratives into Structured Format—0
An Embedding Model for Predicting Roll-Call Votes—0
Adversarial Transfer Learning for Punctuation Restoration—0
A Comparison of Domain-based Word Polarity Estimation using different Word Embeddings—0
Decomposing Word Embedding with the Capsule Network—0
Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements—0
Automatic Term Extraction from Newspaper Corpora: Making the Most of Specificity and Common Features—0
Decoding Word Embeddings with Brain-Based Semantic Features—0
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