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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 2111–2120 of 4002 papers

TitleStatusHype
Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts—0
Empty Category Detection using Path Features and Distributed Case Frames—0
Enabling Cognitive Intelligence Queries in Relational Databases using Low-dimensional Word Embeddings—0
Enabling Open-World Specification Mining via Unsupervised Learning—0
En-Ar Bilingual Word Embeddings without Word Alignment: Factors Effects—0
Encoders Help You Disambiguate Word Senses in Neural Machine Translation—0
Encoding Prior Knowledge with Eigenword Embeddings—0
Encoding Sentiment Information into Word Vectors for Sentiment Analysis—0
End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings—0
ENGLAWI: From Human- to Machine-Readable Wiktionary—0
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