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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 3721–3730 of 4002 papers

TitleStatusHype
Easy-First Dependency Parsing with Hierarchical Tree LSTMs—0
ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification—0
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
EduBERT: Pretrained Deep Language Models for Learning Analytics—0
Effective Context and Fragment Feature Usage for Named Entity Recognition—0
Effective Greedy Inference for Graph-based Non-Projective Dependency Parsing—0
Effectiveness of Deep Networks in NLP using BiDAF as an example architecture—0
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