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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 3791–3800 of 4002 papers

TitleStatusHype
Exploiting Debate Portals for Semi-Supervised Argumentation Mining in User-Generated Web DiscourseCode0
Summarization Based on Embedding Distributions—0
Evaluation of Word Vector Representations by Subspace AlignmentCode0
Supervised Phrase Table Triangulation with Neural Word Embeddings for Low-Resource Languages—0
Evaluation methods for unsupervised word embeddings—0
Specializing Word Embeddings for Similarity or Relatedness—0
Syntactic Dependencies and Distributed Word Representations for Analogy Detection and Mining—0
Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings—0
Empty Category Detection using Path Features and Distributed Case Frames—0
How to Avoid Unwanted Pregnancies: Domain Adaptation using Neural Network Models—0
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