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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 441–450 of 4002 papers

TitleStatusHype
Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization—0
Debiasing Word Embeddings with Nonlinear GeometryCode0
Learning Dynamic Contextualised Word Embeddings via Template-based Temporal AdaptationCode0
Dialogue Term Extraction using Transfer Learning and Topological Data Analysis—0
Lost in Context? On the Sense-wise Variance of Contextualized Word Embeddings—0
Word-Embeddings Distinguish Denominal and Root-Derived Verbs in Semitic—0
Assessing the Unitary RNN as an End-to-End Compositional Model of Syntax—0
Where's the Learning in Representation Learning for Compositional Semantics and the Case of Thematic Fit—0
Large scale analysis of gender bias and sexism in song lyrics—0
Benchmarking zero-shot and few-shot approaches for tokenization, tagging, and dependency parsing of Tagalog text—0
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