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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 2671–2680 of 4002 papers

TitleStatusHype
Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor DetectionCode0
Multi-Module Recurrent Neural Networks with Transfer Learning—0
SB@GU at the Complex Word Identification 2018 Shared Task—0
Computationally Constructed Concepts: A Machine Learning Approach to Metaphor Interpretation Using Usage-Based Construction Grammatical Cues—0
Conditional Random Fields for Metaphor Detection—0
Entropy-Based Subword Mining with an Application to Word Embeddings—0
Fusing Document, Collection and Label Graph-based Representations with Word Embeddings for Text ClassificationCode0
NILC at CWI 2018: Exploring Feature Engineering and Feature Learning—0
Addressing Low-Resource Scenarios with Character-aware Embeddings—0
Using Language Learner Data for Metaphor DetectionCode0
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