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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 3376–3400 of 4002 papers

TitleStatusHype
Improving Neural Knowledge Base Completion with Cross-Lingual Projections—0
Word Sense Filtering Improves Embedding-Based Lexical Substitution—0
Semantic Similarity of Arabic Sentences with Word Embeddings—0
Inducing Embeddings for Rare and Unseen Words by Leveraging Lexical Resources—0
Integrating Semantic Knowledge into Lexical Embeddings Based on Information Content MeasurementCode0
Learning to Negate Adjectives with Bilinear Models—0
Elucidating Conceptual Properties from Word Embeddings—0
Modelling metaphor with attribute-based semantics—0
Centroid-based Text Summarization through Compositionality of Word EmbeddingsCode0
Nonsymbolic Text Representation—0
The Language of Place: Semantic Value from Geospatial Context—0
Automatic Argumentative-Zoning Using Word2vecCode0
Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?—0
An embedded segmental K-means model for unsupervised segmentation and clustering of speechCode0
Dynamic Bernoulli Embeddings for Language EvolutionCode0
Story Cloze Ending Selection Baselines and Data Examination—0
What can you do with a rock? Affordance extraction via word embeddings—0
Unsupervised Learning of Sentence Embeddings using Compositional n-Gram FeaturesCode0
Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use—0
Sound-Word2Vec: Learning Word Representations Grounded in Sounds—0
A Comparative Study of Word Embeddings for Reading Comprehension—0
Dynamic Word Embeddings for Evolving Semantic DiscoveryCode0
Dynamic Word EmbeddingsCode0
Use Generalized Representations, But Do Not Forget Surface Features—0
LTSG: Latent Topical Skip-Gram for Mutually Learning Topic Model and Vector Representations—0
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