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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 2491–2500 of 4002 papers

TitleStatusHype
Incorporating Emoji Descriptions Improves Tweet Classification—0
Incorporating Relational Knowledge into Word Representations using Subspace Regularization—0
Incorporating Sub-Word Level Information in Language Invariant Neural Event Detection—0
Incorporating visual features into word embeddings: A bimodal autoencoder-based approach—0
Incorporating Word Embeddings into Open Directory Project based Large-scale Classification—0
Increasing Sentence-Level Comprehension Through Text Classification of Epistemic Functions—0
Incremental Sense Weight Training for the Interpretation of Contextualized Word Embeddings—0
Exploring Interpretability of Independent Components of Word Embeddings with Automated Word Intruder Test—0
Indigenous Language Revitalization and the Dilemma of Gender Bias—0
In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition—0
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