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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 1311–1320 of 4002 papers

TitleStatusHype
Demonstration of a Literature Based Discovery System based on Ontologies, Semantic Filters and Word Embeddings for the Raynaud Disease-Fish Oil Rediscovery—0
TUE at SemEval-2020 Task 1: Detecting Semantic Change by Clustering Contextual Word Embeddings—0
Go Simple and Pre-Train on Domain-Specific Corpora: On the Role of Training Data for Text Classification—0
Manifold Learning-based Word Representation Refinement Incorporating Global and Local Information—0
An Empirical Study of the Downstream Reliability of Pre-Trained Word Embeddings—0
Blind signal decomposition of various word embeddings based on join and individual variance explained—0
A Deep Content-Based Model for Persian Rumor Verification—0
Automatic coding of students' writing via Contrastive Representation Learning in the Wasserstein space—0
Unsupervised Word Translation Pairing using Refinement based Point Set Registration—0
Improving Biomedical Named Entity Recognition with Syntactic InformationCode0
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