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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 3821–3830 of 4002 papers

TitleStatusHype
Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsing—0
Evaluating Feature Extraction Methods for Knowledge-based Biomedical Word Sense Disambiguation—0
Evaluating Input Representation for Language Identification in Hindi-English Code Mixed Text—0
Evaluating KGR10 Polish word embeddings in the recognition of temporal expressions using BiLSTM-CRF—0
Evaluating Machine Common Sense via Cloze Testing—0
Evaluating Metrics for Bias in Word Embeddings—0
Evaluating Monolingual and Crosslingual Embeddings on Datasets of Word Association Norms—0
Evaluating multi-sense embeddings for semantic resolution monolingually and in word translation—0
Evaluating Natural Alpha Embeddings on Intrinsic and Extrinsic Tasks—0
Evaluating Neural Word Representations in Tensor-Based Compositional Settings—0
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