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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 1561–1570 of 4002 papers

TitleStatusHype
A New Approach to Animacy Detection—0
Fermi at SemEval-2017 Task 7: Detection and Interpretation of Homographic puns in English Language—0
Self-supervised audio representation learning for mobile devices—0
A mostly unlexicalized model for recognizing textual entailment—0
Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF—0
Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach—0
Combining word embeddings and convolutional neural networks to detect duplicated questions—0
Field Embedding: A Unified Grain-Based Framework for Word Representation—0
Figure Eight at SemEval-2019 Task 3: Ensemble of Transfer Learning Methods for Contextual Emotion Detection—0
Detecting Cross-Lingual Plagiarism Using Simulated Word Embeddings—0
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