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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 2201–2250 of 4002 papers

TitleStatusHype
Evaluation of Question Answering Systems: Complexity of judging a natural language—0
Evaluation of Stacked Embeddings for Bulgarian on the Downstream Tasks POS and NERC—0
Evaluation of Taxonomy Enrichment on Diachronic WordNet Versions—0
Evaluation of word embeddings against cognitive processes: primed reaction times in lexical decision and naming tasks—0
Evaluation of Word Embeddings for the Social Sciences—0
Event Detection Using Frame-Semantic Parser—0
Event extraction from Twitter using Non-Parametric Bayesian Mixture Model with Word Embeddings—0
Event Ordering with a Generalized Model for Sieve Prediction Ranking—0
Event Prominence Extraction Combining a Knowledge-Based Syntactic Parser and a BERT Classifier for Dutch—0
Event Role Extraction using Domain-Relevant Word Representations—0
Event Role Labelling using a Neural Network Model (\'Etiquetage en r\^oles \'ev\'enementiels fond\'e sur l'utilisation d'un mod\`ele neuronal) [in French]—0
Evolving Hate Speech Online: An Adaptive Framework for Detection and Mitigation—0
Evolving Large Text Corpora: Four Versions of the Icelandic Gigaword Corpus—0
Exact gradient updates in time independent of output size for the spherical loss family—0
Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework—0
Example-based Acquisition of Fine-grained Collocation Resources—0
ExB Themis: Extensive Feature Extraction from Word Alignments for Semantic Textual Similarity—0
Expanding Subjective Lexicons for Social Media Mining with Embedding Subspaces—0
Expanding the Text Classification Toolbox with Cross-Lingual Embeddings—0
Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity—0
Experimental Evaluation of Deep Learning models for Marathi Text Classification—0
Experiments on a Guarani Corpus of News and Social Media—0
Explainability of Text Processing and Retrieval Methods: A Critical Survey—0
Explainable Identification of Hate Speech towards Islam using Graph Neural Networks—0
Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning—0
Explaining and Generalizing Skip-Gram through Exponential Family Principal Component Analysis—0
Explaining the Trump Gap in Social Distancing Using COVID Discourse—0
Explaining Word Embeddings via Disentangled Representation—0
Exploiting Class Labels to Boost Performance on Embedding-based Text Classification—0
Exploiting Common Characters in Chinese and Japanese to Learn Cross-Lingual Word Embeddings via Matrix Factorization—0
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data—0
Exploiting Morphological Regularities in Distributional Word Representations—0
Exploiting Position and Contextual Word Embeddings for Keyphrase Extraction from Scientific Papers—0
Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion—0
Exploration des relations sémantiques sous-jacentes aux plongements contextuels de mots (Exploring semantic relations underlying contextual word embeddings)—0
Exploration on Grounded Word Embedding: Matching Words and Images with Image-Enhanced Skip-Gram Model—0
Exploring Adequacy Errors in Neural Machine Translation with the Help of Cross-Language Aligned Word Embeddings—0
Exploring Bilingual Word Embeddings for Hiligaynon, a Low-Resource Language—0
Exploring Category Structure with Contextual Language Models and Lexical Semantic Networks—0
Exploring Convolutional Neural Networks for Sentiment Analysis of Spanish tweets—0
Exploring Distributional Representations and Machine Translation for Aspect-based Cross-lingual Sentiment Classification.—0
Exploring Embeddings for Measuring Text Relatedness: Unveiling Sentiments and Relationships in Online Comments—0
Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis—0
Exploring Human Gender Stereotypes with Word Association Test—0
Exploring Input Representation Granularity for Generating Questions Satisfying Question-Answer Congruence—0
Exploring Intra and Inter-language Consistency in Embeddings with ICA—0
Exploring Numeracy in Word Embeddings—0
Exploring Semantic Representation in Brain Activity Using Word Embeddings—0
Exploring sentence informativeness—0
Exploring the Combination of Contextual Word Embeddings and Knowledge Graph Embeddings—0
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