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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 1851–1900 of 4002 papers

TitleStatusHype
BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model PerformanceCode0
Mapping Supervised Bilingual Word Embeddings from English to low-resource languagesCode0
Transformers without Tears: Improving the Normalization of Self-AttentionCode0
Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy LabelsCode0
From the Paft to the Fiiture: a Fully Automatic NMT and Word Embeddings Method for OCR Post-CorrectionCode0
IdBench: Evaluating Semantic Representations of Identifier Names in Source CodeCode0
Multi-label Categorization of Accounts of Sexism using a Neural FrameworkCode0
When Specialization Helps: Using Pooled Contextualized Embeddings to Detect Chemical and Biomedical Entities in Spanish—0
Aligning Multilingual Word Embeddings for Cross-Modal Retrieval TaskCode0
Correlations between Word Vector SetsCode0
On Dimensional Linguistic Properties of the Word Embedding SpaceCode0
DialectGram: Detecting Dialectal Variation at Multiple Geographic ResolutionsCode0
Investigating the Effectiveness of Representations Based on Word-Embeddings in Active Learning for Labelling Text DatasetsCode0
Complex networks based word embeddings—0
Extracting UMLS Concepts from Medical Text Using General and Domain-Specific Deep Learning Models—0
Improving Word Embedding Factorization for Compression Using Distilled Nonlinear Neural Decomposition—0
Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word RepresentationsCode0
Language-Agnostic Visual-Semantic EmbeddingsCode0
Additional Shared Decoder on Siamese Multi-view Encoders for Learning Acoustic Word Embeddings—0
Specializing Word Embeddings (for Parsing) by Information BottleneckCode0
Utilizing Word Embeddings based Features for Phylogenetic Tree Generation of Sanskrit Texts—0
Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models—0
Learning Category Correlations for Multi-label Image Recognition with Graph Networks—0
On the Importance of Subword Information for Morphological Tasks in Truly Low-Resource Languages—0
Atalaya at TASS 2019: Data Augmentation and Robust Embeddings for Sentiment Analysis—0
Learn Interpretable Word Embeddings Efficiently with von Mises-Fisher Distribution—0
On Understanding Knowledge Graph Representation—0
Extremely Small BERT Models from Mixed-Vocabulary Training—0
Classification Attention for Chinese NER—0
Multi-source Multi-view Transfer Learning in Neural Topic Modeling with Pretrained Topic and Word Embeddings—0
Distilled embedding: non-linear embedding factorization using knowledge distillation—0
Interpreting Knowledge Graph Relation Representation from Word Embeddings—0
DeepXML: Scalable & Accurate Deep Extreme Classification for Matching User Queries to Advertiser Bid Phrases—0
Code-switching Language Modeling With Bilingual Word Embeddings: A Case Study for Egyptian Arabic-English—0
Deep Text Mining of Instagram Data Without Strong SupervisionCode0
GNTeam at 2018 n2c2: Feature-augmented BiLSTM-CRF for drug-related entity recognition in hospital discharge summariesCode0
Generating Timelines by Modeling Semantic ChangeCode0
Low-Rank Approximation of Matrices for PMI-based Word Embeddings—0
Cross-lingual Dependency Parsing with Unlabeled Auxiliary LanguagesCode0
Triplet-Aware Scene Graph Embeddings—0
Multi-sense Definition Modeling using Word Sense Decompositions—0
CogniVal: A Framework for Cognitive Word Embedding EvaluationCode0
Decision-Directed Data DecompositionCode0
Cross-Lingual Contextual Word Embeddings Mapping With Multi-Sense Words In Mind—0
Corporate IT-support Help-Desk Process Hybrid-Automation Solution with Machine Learning Approach—0
Hierarchical Meta-Embeddings for Code-Switching Named Entity RecognitionCode0
Do We Need Neural Models to Explain Human Judgments of Acceptability?—0
Multi Sense Embeddings from Topic Models—0
Cross-Lingual BERT Transformation for Zero-Shot Dependency ParsingCode0
Multi-view and Multi-source Transfers in Neural Topic Modeling with Pretrained Topic and Word Embeddings—0
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