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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 1801–1825 of 4002 papers

TitleStatusHype
Implicit Phenomena in Short-answer Scoring Data—0
Implicit Subjective and Sentimental Usages in Multi-sense Word Embeddings—0
INAOE-UPV at SemEval-2018 Task 3: An Ensemble Approach for Irony Detection in Twitter—0
Importance of Self-Attention for Sentiment Analysis—0
In-Context Former: Lightning-fast Compressing Context for Large Language Model—0
Improved and Robust Controversy Detection in General Web Pages Using Semantic Approaches under Large Scale Conditions—0
Incorporating Word Embeddings into Open Directory Project based Large-scale Classification—0
Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension—0
Improved CCG Parsing with Semi-supervised Supertagging—0
Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data—0
Improved Neural Network-based Multi-label Classification with Better Initialization Leveraging Label Co-occurrence—0
CopyBERT: A Unified Approach to Question Generation with Self-Attention—0
Improved Semantic Representation for Domain-Specific Entities—0
Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention—0
Improved Text Classification via Contrastive Adversarial Training—0
Improved Word Embeddings with Implicit Structure Information—0
Corpus specificity in LSA and Word2vec: the role of out-of-domain documents—0
Analyzing Semantic Change in Japanese Loanwords—0
Improve Lexicon-based Word Embeddings By Word Sense Disambiguation—0
Correcting the Common Discourse Bias in Linear Representation of Sentences using Conceptors—0
Improving Aspect-Level Sentiment Analysis with Aspect Extraction—0
Improving average ranking precision in user searches for biomedical research datasets—0
Des pseudo-sens pour am\'eliorer l'extraction de synonymes \`a partir de plongements lexicaux (Pseudo-senses for improving the extraction of synonyms from word embeddings)—0
Improving Biomedical Analogical Retrieval with Embedding of Structural Dependencies—0
Designing a Russian Idiom-Annotated Corpus—0
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