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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 1461–1470 of 4002 papers

TitleStatusHype
A Multi-Resolution Word Embedding for Document Retrieval from Large Unstructured Knowledge Bases—0
Composing Noun Phrase Vector Representations—0
Composing Knowledge Graph Embeddings via Word Embeddings—0
Component-Enhanced Chinese Character Embeddings—0
Assessing the Unitary RNN as an End-to-End Compositional Model of Syntax—0
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations—0
A Deep Learning approach for Hindi Named Entity Recognition—0
Complex Ontology Matching with Large Language Model Embeddings—0
Complex networks based word embeddings—0
Assessing the Corpus Size vs. Similarity Trade-off for Word Embeddings in Clinical NLP—0
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