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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 3611–3620 of 4002 papers

TitleStatusHype
Detecting Most Frequent Sense using Word Embeddings and BabelNet—0
Detecting Paraphrases of Standard Clause Titles in Insurance Contracts—0
Detecting Policy Preferences and Dynamics in the UN General Debate with Neural Word Embeddings—0
Detecting Requirements Smells With Deep Learning: Experiences, Challenges and Future Work—0
Detecting Sarcasm Using Different Forms Of Incongruity—0
Detecting Semantically Equivalent Questions in Online User Forums—0
Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to Modeling—0
Detecting Unseen Multiword Expressions in American Sign Language—0
Detecting weak and strong Islamophobic hate speech on social media—0
Detection of Adverse Drug Reaction in Tweets Using a Combination of Heterogeneous Word Embeddings—0
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