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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 3701–3710 of 4002 papers

TitleStatusHype
Lexical Coherence Graph Modeling Using Word Embeddings—0
Ten Pairs to Tag -- Multilingual POS Tagging via Coarse Mapping between Embeddings—0
English Resource Semantics—0
Symmetric Patterns and Coordinations: Fast and Enhanced Representations of Verbs and Adjectives—0
LSTM CCG Parsing—0
Automatically Inferring Implicit Properties in Similes—0
Learning a POS tagger for AAVE-like language—0
Diachronic Word Embeddings Reveal Statistical Laws of Semantic ChangeCode0
Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction—0
Query Expansion with Locally-Trained Word Embeddings—0
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