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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 3121–3130 of 4002 papers

TitleStatusHype
A Simple Disaster-Related Knowledge Base for Intelligent Agents—0
A Simple Fully Connected Network for Composing Word Embeddings from Characters—0
A Simple Language Model based on PMI Matrix Approximations—0
A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings—0
A Simple Word Embedding Model for Lexical Substitution—0
Ask the GRU: Multi-Task Learning for Deep Text Recommendations—0
ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity—0
Aspect-based Opinion Summarization with Convolutional Neural Networks—0
Aspect-Based Sentiment Analysis Using Bitmask Bidirectional Long Short Term Memory Networks—0
AsPOS: Assamese Part of Speech Tagger using Deep Learning Approach—0
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