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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 2476–2500 of 4002 papers

TitleStatusHype
Towards Optimal Transport with Global Invariances—0
Towards Qualitative Word Embeddings Evaluation: Measuring Neighbors Variation—0
Towards Resolving Word Ambiguity with Word Embeddings—0
Towards Smart Point-and-Shoot Photography—0
Towards the Understanding of Gaming Audiences by Modeling Twitch Emotes—0
Towards Unsupervised Text Classification Leveraging Experts and Word Embeddings—0
Toward Understanding Bias Correlations for Mitigation in NLP—0
Toward Word Embedding for Personalized Information Retrieval—0
TRAC-1 Shared Task on Aggression Identification: IIT(ISM)@COLING'18—0
Tracing armed conflicts with diachronic word embedding models—0
Tracing variation in discourse connectives in translation and interpreting through neural semantic spaces—0
Tracking Changes in ESG Representation: Initial Investigations in UK Annual Reports—0
Tracking Semantic Change in Cognate Sets for English and Romance Languages—0
Tracking the Evolution of Words with Time-reflective Text Representations—0
Tracking the progress of Language Models by extracting their underlying Knowledge Graphs—0
Traffic event description based on Twitter data using Unsupervised Learning Methods for Indian road conditions—0
Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images—0
Training Word Sense Embeddings With Lexicon-based Regularization—0
Transcending the "Male Code": Implicit Masculine Biases in NLP Contexts—0
TransDrift: Modeling Word-Embedding Drift using Transformer—0
Transfer and Multi-Task Learning for Noun--Noun Compound Interpretation—0
Transfer Learning across Low-Resource, Related Languages for Neural Machine Translation—0
Transfer Learning in Natural Language Processing—0
Transferred Embeddings for Igbo Similarity, Analogy, and Diacritic Restoration Tasks—0
Transferring Coreference Resolvers with Posterior Regularization—0
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