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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 1051–1060 of 4002 papers

TitleStatusHype
Discourse Relation Embeddings: Representing the Relations between Discourse Segments in Social MediaCode0
Deep Unordered Composition Rivals Syntactic Methods for Text ClassificationCode0
Deep word embeddings for visual speech recognitionCode0
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling MechanismsCode0
Discovering emergent connections in quantum physics research via dynamic word embeddingsCode0
Do Acoustic Word Embeddings Capture Phonological Similarity? An Empirical StudyCode0
Do We Really Need All Those Rich Linguistic Features? A Neural Network-Based Approach to Implicit Sense LabelingCode0
Definition Modeling: Learning to define word embeddings in natural languageCode0
A Neural Generative Model for Joint Learning Topics and Topic-Specific Word EmbeddingsCode0
Dict2vec : Learning Word Embeddings using Lexical DictionariesCode0
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