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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 2901–2950 of 4002 papers

TitleStatusHype
A Deeper Look into Dependency-Based Word Embeddings—0
A Deep Fusion Model for Domain Adaptation in Phrase-based MT—0
A Deep Learning approach for Hindi Named Entity Recognition—0
A Deep Learning Approach to Behavior-Based Learner Modeling—0
A Deep Learning Architecture for De-identification of Patient Notes: Implementation and Evaluation—0
A Deep Learning System for Automatic Extraction of Typological Linguistic Information from Descriptive Grammars—0
A Deep Neural Framework for Contextual Affect Detection—0
A Deep Representation Empowered Distant Supervision Paradigm for Clinical Information Extraction—0
A Deterministic Algorithm for Bridging Anaphora Resolution—0
A Distribution-based Model to Learn Bilingual Word Embeddings—0
Adjusting Word Embeddings with Semantic Intensity Orders—0
A Domain Adaptation Regularization for Denoising Autoencoders—0
A Dual Embedding Space Model for Document Ranking—0
Adullam at SemEval-2017 Task 4: Sentiment Analyzer Using Lexicon Integrated Convolutional Neural Networks with Attention—0
Advancing Fake News Detection: Hybrid DeepLearning with FastText and Explainable AI—0
Advancing Humor-Focused Sentiment Analysis through Improved Contextualized Embeddings and Model Architecture—0
Adversarial Contrastive Estimation—0
Adversarial Evaluation of BERT for Biomedical Named Entity Recognition—0
Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER—0
Adversarial Representation Learning for Text-to-Image Matching—0
Adversarial Training for Unsupervised Bilingual Lexicon Induction—0
Adversarial Transfer Learning for Punctuation Restoration—0
A Dynamic Window Neural Network for CCG Supertagging—0
A Fast and Accurate Dependency Parser using Neural Networks—0
A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval—0
A Feature Analysis for Multimodal News Retrieval—0
Aff2Vec: Affect--Enriched Distributional Word Representations—0
Affect as a proxy for literary mood—0
Affect Enriched Word Embeddings for News Information Retrieval—0
Affective Neural Response Generation—0
Affordance Extraction and Inference based on Semantic Role Labeling—0
A Framework for Decoding Event-Related Potentials from Text—0
A Framework for Developing and Evaluating Word Embeddings of Drug-named Entity—0
A Framework for Understanding the Role of Morphology in Universal Dependency Parsing—0
A General Framework for Detecting Metaphorical Collocations—0
A General Method for Event Detection on Social Media—0
Aggregating Continuous Word Embeddings for Information Retrieval—0
Aggregating User-Centric and Post-Centric Sentiments from Social Media for Topical Stance Prediction—0
Aggression Identification and Multi Lingual Word Embeddings—0
A Gradient Boosting-Seq2Seq System for Latin POS Tagging and Lemmatization—0
A Graph-based Coarse-to-fine Method for Unsupervised Bilingual Lexicon Induction—0
A Helping Hand: Transfer Learning for Deep Sentiment Analysis—0
A Hierarchical Knowledge Representation for Expert Finding on Social Media—0
A Hmong Corpus with Elaborate Expression Annotations—0
A House United: Bridging the Script and Lexical Barrier between Hindi and Urdu—0
A Hybrid Learning Scheme for Chinese Word Embedding—0
AI-KU at SemEval-2016 Task 11: Word Embeddings and Substring Features for Complex Word Identification—0
A Joint Model for Word Embedding and Word Morphology—0
A k-Nearest Neighbor Approach towards Multi-level Sequence Labeling—0
A Language-Based Approach to Fake News Detection Through Interpretable Features and BRNN—0
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