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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 3281–3290 of 4002 papers

TitleStatusHype
Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich LanguagesCode0
The Importance of Automatic Syntactic Features in Vietnamese Named Entity Recognition—0
ASR error management for improving spoken language understanding—0
Second-Order Word Embeddings from Nearest Neighbor Topological FeaturesCode0
Contextualizing Citations for Scientific Summarization using Word Embeddings and Domain Knowledge—0
Lightweight Efficient Multi-keyword Ranked Search over Encrypted Cloud Data using Dual Word Embeddings—0
Learning Semantic Relatedness From Human Feedback Using Metric Learning—0
Mixed Membership Word Embeddings for Computational Social Science—0
Utility of General and Specific Word Embeddings for Classifying Translational Stages of Research—0
Evaluating vector-space models of analogy—0
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