Efficient Vector Representation for Documents through Corruption
This work addresses the need for efficient and high-quality document representations for NLP tasks like sentiment analysis and classification, offering a simple yet effective method that is incremental over existing approaches.
The authors tackled the problem of learning efficient document representations by introducing Doc2VecC, a framework that uses a corruption model for regularization, resulting in word embeddings that significantly outperform Word2Vec and match or exceed state-of-the-art methods in tasks like sentiment analysis and document classification, with training speeds of billions of words per hour on a single machine.
We present an efficient document representation learning framework, Document Vector through Corruption (Doc2VecC). Doc2VecC represents each document as a simple average of word embeddings. It ensures a representation generated as such captures the semantic meanings of the document during learning. A corruption model is included, which introduces a data-dependent regularization that favors informative or rare words while forcing the embeddings of common and non-discriminative ones to be close to zero. Doc2VecC produces significantly better word embeddings than Word2Vec. We compare Doc2VecC with several state-of-the-art document representation learning algorithms. The simple model architecture introduced by Doc2VecC matches or out-performs the state-of-the-art in generating high-quality document representations for sentiment analysis, document classification as well as semantic relatedness tasks. The simplicity of the model enables training on billions of words per hour on a single machine. At the same time, the model is very efficient in generating representations of unseen documents at test time.