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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 1461–1470 of 4002 papers

TitleStatusHype
Context Reinforced Neural Topic Modeling over Short TextsCode0
A Neural Generative Model for Joint Learning Topics and Topic-Specific Word EmbeddingsCode0
An exploration of the encoding of grammatical gender in word embeddings—0
Deep Learning based Topic Analysis on Financial Emerging Event Tweets—0
Combining Representations For Effective Citation ClassificationCode0
Unsupervised Deep Cross-modality Spectral Hashing—0
Word embedding and neural network on grammatical gender -- A case study of Swedish—0
Effect of Text Processing Steps on Twitter Sentiment Classification using Word Embedding—0
Word Embeddings: Stability and Semantic Change—0
IITK at the FinSim Task: Hypernym Detection in Financial Domain via Context-Free and Contextualized Word Embeddings—0
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