Distributed Representations of Sentences and Documents (1405.4053v2)

Abstract: Many machine learning algorithms require the input to be represented as a fixed-length feature vector. When it comes to texts, one of the most common fixed-length features is bag-of-words. Despite their popularity, bag-of-words features have two major weaknesses: they lose the ordering of the words and they also ignore semantics of the words. For example, "powerful," "strong" and "Paris" are equally distant. In this paper, we propose Paragraph Vector, an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences, paragraphs, and documents. Our algorithm represents each document by a dense vector which is trained to predict words in the document. Its construction gives our algorithm the potential to overcome the weaknesses of bag-of-words models. Empirical results show that Paragraph Vectors outperform bag-of-words models as well as other techniques for text representations. Finally, we achieve new state-of-the-art results on several text classification and sentiment analysis tasks.

• The paper introduces Paragraph Vector, a method that generates dense, fixed-length text representations via neural networks.
• It presents two models, PV-DM and PV-DBOW, to capture word order and semantic context effectively.
• Empirical results demonstrate improved sentiment analysis and retrieval performance compared to traditional bag-of-words models.

Distributed Representations of Sentences and Documents

The paper "Distributed Representations of Sentences and Documents" by Quoc Le and Tomas Mikolov introduces a novel approach called Paragraph Vector, aimed at creating fixed-length vector representations for variable-length text excerpts like sentences, paragraphs, and documents. This method addresses notable deficiencies in traditional text representations, such as bag-of-words (BOW) and bag-of-n-grams, by preserving semantic and syntactic information.

Introduction

Traditional text representation methods, especially BOW, suffer due to their inability to maintain word order and semantics. This leads to an inadequate representation where semantically similar words are treated equivalently to completely unrelated words. The Paragraph Vector method proposed in this paper aims to rectify these issues while providing an unsupervised approach for learning dense vector representations of varying text lengths.

Methodology

The Paragraph Vector algorithm operates by training a dense vector representation for each document (paragraph) to predict words within the document. The representation is learned via a neural network-based approach, using stochastic gradient descent and backpropagation. The algorithm can be utilized in two modes:

1. Distributed Memory Model (PV-DM): Combines paragraph vectors with context word vectors to predict the next word.
2. Distributed Bag of Words Model (PV-DBOW): Ignores context words and directly predicts words from the paragraph vector.

Key Features:

• Semantic Preservation: Ensures words like "strong" and "powerful" are closely represented in the vector space compared to "Paris".
• Order Sensitivity: Maintains small context word order, competing with higher-dimensional n-gram models but with efficient generalization.

Experiments

The methodology was empirically validated on two primary tasks: sentiment analysis and information retrieval.

Sentiment Analysis:

• Stanford Sentiment Treebank: Achieved state-of-the-art error rates of 12.2% (coarse-grained) and 51.3% (fine-grained). The paper demonstrated significant improvement over models relying heavily on parsing and compositionality.
• IMDB Dataset: The model notably reduced the error rate to 7.42%, outperforming previous methods that combined bag-of-words with sophisticated machine learning models.

Information Retrieval:

• The Paragraph Vector was tested on an information retrieval task, significantly outperforming BOW and bigram models. The Paragraph Vector approach resulted in an error rate of 3.82% compared to the next best result of 5.67% with weighted bag-of-bigrams.

Theoretical and Practical Implications

The Paragraph Vector method not only provides robust text representations capable of outperforming traditional methods in various classification tasks, but it also promises broader applications. In practical terms, Paragraph Vector can be directly integrated into conventional machine learning pipelines, facilitating tasks requiring text comprehension without depending on labeled data.

Theoretically, the approach presents a framework for representing sequential data across various domains beyond text, potentially extending to applications in bioinformatics and beyond.

Future Developments

The future trajectory of research following this paper could involve:

• Scaling and Efficiency: Improving computational efficiency for large-scale data sets and real-time applications.
• Cross-Domain Applications: Exploring the use of Paragraph Vector in non-textual sequential data.
• Refinement of Architectures: Enhancing the models through hybrid approaches combining PV-DM and PV-DBOW, or exploring alternative neural network architectures.

In summary, the Paragraph Vector algorithm posited by Le and Mikolov represents a significant step forward in the domain of text representation. By addressing critical weaknesses of bag-of-words models, this method enhances the capability of machine learning algorithms to process and understand natural language effectively. The empirical results underscore the practicality and efficacy of the approach, paving the way for its adoption in a variety of text analysis tasks.