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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 2826–2850 of 4002 papers

TitleStatusHype
Zero-Shot Cross-Lingual Opinion Target Extraction—0
Zero-Shot Cross-Lingual Transfer is a Hard Baseline to Beat in German Fine-Grained Entity Typing—0
Zero-Shot Visual Question Answering—0
Text Classification Components for Detecting Descriptions and Names of CAD models—0
Zur Darstellung eines mehrstufigen Prototypbegriffs in der multilingualen automatischen Sprachgenerierung: vom Korpus über word embeddings bis hin zum automatischen Wörterbuch—0
Text2Node: a Cross-Domain System for Mapping Arbitrary Phrases to a Taxonomy—0
Self-supervised audio representation learning for mobile devices—0
Emotional Embeddings: Refining Word Embeddings to Capture Emotional Content of Words—0
Examining Structure of Word Embeddings with PCA—0
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains—0
On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning—0
Disentangling Latent Emotions of Word Embeddings on Complex Emotional Narratives—0
Parsimonious Morpheme Segmentation with an Application to Enriching Word Embeddings—0
Low-Rank Approximation of Matrices for PMI-based Word Embeddings—0
1Cademy at Semeval-2022 Task 1: Investigating the Effectiveness of Multilingual, Multitask, and Language-Agnostic Tricks for the Reverse Dictionary Task—0
Misinforming LLMs: vulnerabilities, challenges and opportunities—0
Decoupled Vocabulary Learning Enables Zero-Shot Translation from Unseen Languages—0
A Simplified Retriever to Improve Accuracy of Phenotype Normalizations by Large Language Models—0
300-sparsans at SemEval-2018 Task 9: Hypernymy as interaction of sparse attributes—0
4chan & 8chan embeddings—0
A bag-of-concepts model improves relation extraction in a narrow knowledge domain with limited data—0
A Bayesian approach to uncertainty in word embedding bias estimation—0
ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNet—0
Abelian Neural Networks—0
A bilingual approach to specialised adjectives through word embeddings in the karstology domain—0
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