Class Vectors: Embedding representation of Document Classes

Distributed representations of words and paragraphs as semantic embeddings in high dimensional data are used across a number of Natural Language Understanding tasks such as retrieval, translation, and classification. In this work, we propose "Class Vectors" - a framework for learning a vec...

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Bibliographic Details
Published in:arXiv.org 2015-08
Main Authors: Devendra Singh Sachan, Kumar, Shailesh
Format: Article
Language:English
Subjects:
Classification
Data mining
Electronic commerce
Embedding
Product reviews
Representations
Sentiment analysis
Online Access:Get full text
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Summary:Distributed representations of words and paragraphs as semantic embeddings in high dimensional data are used across a number of Natural Language Understanding tasks such as retrieval, translation, and classification. In this work, we propose "Class Vectors" - a framework for learning a vector per class in the same embedding space as the word and paragraph embeddings. Similarity between these class vectors and word vectors are used as features to classify a document to a class. In experiment on several sentiment analysis tasks such as Yelp reviews and Amazon electronic product reviews, class vectors have shown better or comparable results in classification while learning very meaningful class embeddings.
ISSN:2331-8422