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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 2826–2850 of 4002 papers

TitleStatusHype
Lucene for Approximate Nearest-Neighbors Search on Arbitrary Dense Vectors—0
Lump at SemEval-2017 Task 1: Towards an Interlingua Semantic Similarity—0
LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and Quantification—0
Machine Comprehension with Syntax, Frames, and Semantics—0
Machine Learning Based on Natural Language Processing to Detect Cardiac Failure in Clinical Narratives—0
Machine Learning to Promote Translational Research: Predicting Patent and Clinical Trial Inclusion in Dementia Research—0
Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings—0
Machine Translation for Accessible Multi-Language Text Analysis—0
Machine Translation for English–Inuktitut with Segmentation, Data Acquisition and Pre-Training—0
Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query-based summarisation—0
Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query-based multi-document summarisation—0
MainiwayAI at IJCNLP-2017 Task 2: Ensembles of Deep Architectures for Valence-Arousal Prediction—0
Manifold Learning-based Word Representation Refinement Incorporating Global and Local Information—0
Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs—0
Mapping Unparalleled Clinical Professional and Consumer Languages with Embedding Alignment—0
Mapping Unseen Words to Task-Trained Embedding Spaces—0
MappSent: a Textual Mapping Approach for Question-to-Question Similarity—0
Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yor\`ub\'a and Twi—0
MatNexus: A Comprehensive Text Mining and Analysis Suite for Materials Discover—0
MayoNLP at SemEval 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications—0
MDR Cluster-Debias: A Nonlinear WordEmbedding Debiasing Pipeline—0
Meaning at the Planck scale? Contextualized word embeddings for doing history, philosophy, and sociology of science—0
Meaning\_space at SemEval-2018 Task 10: Combining explicitly encoded knowledge with information extracted from word embeddings—0
MEANT 2.0: Accurate semantic MT evaluation for any output language—0
Measure and Evaluation of Semantic Divergence across Two Languages—0
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