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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 2441–2450 of 4002 papers

TitleStatusHype
IITP at EmoInt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized Features—0
IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text—0
Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search—0
Image Captioning using Deep Stacked LSTMs, Contextual Word Embeddings and Data Augmentation—0
Image Captioning with Visual Object Representations Grounded in the Textual Modality—0
Impact of Gender Debiased Word Embeddings in Language Modeling—0
Impart Contextualization to Static Word Embeddings through Semantic Relations—0
Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network—0
Implicit Phenomena in Short-answer Scoring Data—0
Implicit Subjective and Sentimental Usages in Multi-sense Word Embeddings—0
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