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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 3531–3540 of 4002 papers

TitleStatusHype
Supervised Word Sense Disambiguation with Sentences Similarities from Context Word Embeddings—0
Topic Modeling over Short Texts by Incorporating Word EmbeddingsCode0
Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks—0
emoji2vec: Learning Emoji Representations from their DescriptionCode0
Creating Causal Embeddings for Question Answering with Minimal Supervision—0
Ask the GRU: Multi-Task Learning for Deep Text Recommendations—0
Content Selection through Paraphrase Detection: Capturing different Semantic Realisations of the Same Idea—0
Hash2Vec, Feature Hashing for Word Embeddings—0
Testing APSyn against Vector Cosine on Similarity Estimation—0
Learning Word Embeddings from Intrinsic and Extrinsic Views—0
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