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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 1181–1190 of 4002 papers

TitleStatusHype
Distributed Vector Representations for Unsupervised Automatic Short Answer Grading—0
Distributed Training of Embeddings using Graph Analytics—0
Development of Word Embeddings for Uzbek Language—0
Distributional Hypernym Generation by Jointly Learning Clusters and Projections—0
Distributional Lesk: Effective Knowledge-Based Word Sense Disambiguation—0
Distributional regularities of verbs and verbal adjectives: Treebank evidence and broader implications—0
An exploration of the encoding of grammatical gender in word embeddings—0
Distributional semantic modeling: a revised technique to train term/word vector space models applying the ontology-related approach—0
Distributional Semantics for Neo-Latin—0
Development of a Japanese Personality Dictionary based on Psychological Methods—0
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