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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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TitleStatusHype
Word embedding and neural network on grammatical gender -- A case study of Swedish—0
Word Embedding and WordNet Based Metaphor Identification and Interpretation—0
Word Embedding-based Antonym Detection using Thesauri and Distributional Information—0
Word Embedding-Based Automatic MT Evaluation Metric using Word Position Information—0
Word-Embedding based Content Features for Automated Oral Proficiency Scoring—0
Word Embedding Calculus in Meaningful Ultradense Subspaces—0
Word Embedding Evaluation and Combination—0
Word Embedding Evaluation Datasets and Wikipedia Title Embedding for Chinese—0
Word Embedding Evaluation for Sinhala—0
Word Embedding Evaluation in Downstream Tasks and Semantic Analogies—0
Word Embeddings, Analogies, and Machine Learning: Beyond king - man + woman = queen—0
Word embeddings and discourse information for Quality Estimation—0
Word embeddings and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation—0
Word Embeddings and Their Use In Sentence Classification Tasks—0
Word Embeddings and Validity Indexes in Fuzzy Clustering—0
Word Embeddings as Features for Supervised Coreference Resolution—0
Word Embeddings as Metric Recovery in Semantic Spaces—0
Word Embeddings as Tuples of Feature Probabilities—0
Word Embeddings: A Survey—0
Word Embeddings based on Fixed-Size Ordinally Forgetting Encoding—0
Word Embeddings-Based Uncertainty Detection in Financial Disclosures—0
Word Embeddings, Cosine Similarity and Deep Learning for Identification of Professions & Occupations in Health-related Social Media—0
Word-Embeddings Distinguish Denominal and Root-Derived Verbs in Semitic—0
Word Embeddings for Banking Industry—0
Word Embeddings for Chemical Patent Natural Language Processing—0
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