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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 1551–1560 of 4002 papers

TitleStatusHype
Fast query-by-example speech search using separable model—0
Fast Query Expansion on an Accounting Corpus using Sub-Word Embeddings—0
Extremely Small BERT Models from Mixed-Vocabulary Training—0
FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings—0
Combining BERT with Static Word Embeddings for Categorizing Social Media—0
A Sequence Learning Method for Domain-Specific Entity Linking—0
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages—0
Feature Engineering vs BERT on Twitter Data—0
Addressing Low-Resource Scenarios with Character-aware Embeddings—0
A Chinese Writing Correction System for Learning Chinese as a Foreign Language—0
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