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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 781–790 of 4002 papers

TitleStatusHype
Definition Frames: Using Definitions for Hybrid Concept RepresentationsCode0
DefSent+: Improving sentence embeddings of language models by projecting definition sentences into a quasi-isotropic or isotropic vector space of unlimited dictionary entriesCode0
Def2Vec: Extensible Word Embeddings from Dictionary DefinitionsCode0
Combining Word Embeddings and Feature Embeddings for Fine-grained Relation ExtractionCode0
Learning and Evaluating Character Representations in NovelsCode0
Deep word embeddings for visual speech recognitionCode0
Learning Contextual Tag Embeddings for Cross-Modal Alignment of Audio and TagsCode0
Learning Crosslingual Word Embeddings without Bilingual CorporaCode0
Degree-Aware Alignment for Entities in TailCode0
Bidirectional LSTM-CRF for Clinical Concept ExtractionCode0
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