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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 1251–1260 of 4002 papers

TitleStatusHype
ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection—0
ECNU: Leveraging Word Embeddings to Boost Performance for Paraphrase in Twitter—0
ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation—0
EDGAR-CORPUS: Billions of Tokens Make The World Go Round—0
A Framework for Understanding the Role of Morphology in Universal Dependency Parsing—0
EduBERT: Pretrained Deep Language Models for Learning Analytics—0
Effective Context and Fragment Feature Usage for Named Entity Recognition—0
BOUN-ISIK Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes—0
Effective Greedy Inference for Graph-based Non-Projective Dependency Parsing—0
Determining Gains Acquired from Word Embedding Quantitatively Using Discrete Distribution Clustering—0
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