Efficient Vector Representation for Documents through Corruption

Abstract: 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.

• The paper introduces Doc2VecC, a model that averages word embeddings with a corruption mechanism to create efficient document representations.
• The method employs data-dependent regularization by corrupting input words, emphasizing informative terms over common ones.
• Empirical evaluations demonstrate improved performance on sentiment analysis, document classification, and semantic relatedness tasks compared to traditional models.

Efficient Document Representation via Corruption: Introducing Doc2VecC

Introduction to Doc2VecC

In the domain of natural language processing, the challenge of representing text in a format that a machine can process and understand is pivotal. Traditional models such as Bag-of-Words (BoW) and its extensions, while being straightforward, often fail to capture the semantic relationships between words due to their discrete nature. The neural network-based model, Word2Vec, made significant strides by producing word embeddings that encapsulate word similarities in a continuous vector space. Extending beyond word embeddings, Paragraph Vectors proposed a method to learn document-level representations. However, it suffers from scalability issues and inefficiency in processing unseen documents.

Addressing these limitations, the paper introduces an efficient document representation model named Document Vector through Corruption (Doc2VecC). This model uniquely combines the simplicity of averaging word embeddings with a corruption mechanism that effectively regularizes the training process. This approach not only simplifies the model architecture, making it highly efficient for training and inference but also surpasses the performance of state-of-the-art models on various tasks related to sentiment analysis, document classification, and semantic relatedness.

Model Architecture and Learning Process

Core Mechanism

Doc2VecC represents a document as the average of its word embeddings, incorporating a corruption model in the learning phase. Unlike Paragraph Vectors, Doc2VecC's complexity does not scale with the corpus size, making it highly efficient. The corruption model randomly removes words from the document during learning, aiding in faster training and introducing a form of data-dependent regularization.

Regularization Through Corruption

A key innovation of Doc2VecC is its corruption mechanism, acting as a data-dependent regularization strategy. This method preferentially selects informative or rare words over common, non-discriminative ones by sampling and corrupting document representations during training. This process leads to a regularization effect that inherently minimizes the embeddings of frequent but less informative words.

Empirical Evaluation

Sentiment Analysis and Document Classification

Doc2VecC demonstrates superior performance in sentiment analysis tasks and document classification, often exceeding the capabilities of both traditional and neural network-based models. Notably, it presents a significant efficiency advantage during the inference phase, capable of quickly generating representations for unseen documents.

Semantic Relatedness and Word Analogy

In tasks measuring semantic relatedness between documents, Doc2VecC shows strong performance, albeit slightly trailing behind some LSTM-based models. However, it outperforms the celebrated skip-thought vectors in handling longer documents. For the word analogy task, Doc2VecC's embeddings conspicuously outdo those generated by Word2Vec, underlining the model's effectiveness in capturing nuanced semantic and syntactic relationships.

Final Thoughts and Speculation on Future Developments

The introduction of Doc2VecC marks a significant advancement in document representation learning, simplifying the learning process while enhancing efficiency and performance. Its unique corruption mechanism serves as an effective regularization strategy, promoting the significance of informative words within document representations.

Looking forward, the foundational principles of Doc2VecC may inspire further research and development in document representation models, potentially focusing on more sophisticated corruption mechanisms or exploring varied applications across natural language understanding tasks. Moreover, the interplay between word and document embeddings in capturing semantic relationships presents intriguing avenues for exploration, possibly paving the way for advancements in unsupervised learning for document understanding and information retrieval.

In summary, Doc2VecC's elegant architecture, combined with its impressive performance across different metrics, underscores its value as a formidable tool in the evolving landscape of natural language processing and machine learning.