Document Embedding with Paragraph Vectors

Abstract: Paragraph Vectors has been recently proposed as an unsupervised method for learning distributed representations for pieces of texts. In their work, the authors showed that the method can learn an embedding of movie review texts which can be leveraged for sentiment analysis. That proof of concept, while encouraging, was rather narrow. Here we consider tasks other than sentiment analysis, provide a more thorough comparison of Paragraph Vectors to other document modelling algorithms such as Latent Dirichlet Allocation, and evaluate performance of the method as we vary the dimensionality of the learned representation. We benchmarked the models on two document similarity data sets, one from Wikipedia, one from arXiv. We observe that the Paragraph Vector method performs significantly better than other methods, and propose a simple improvement to enhance embedding quality. Somewhat surprisingly, we also show that much like word embeddings, vector operations on Paragraph Vectors can perform useful semantic results.

• The paper introduces Paragraph Vectors as an innovative method to capture semantic document features, outperforming traditional models in text similarity tasks.
• It details two architectures—the Distributed Memory and the Distributed Bag of Words models—with a focus on the latter for its computational efficiency.
• Experimental evaluations on Wikipedia and arXiv datasets show high accuracy and robustness, supporting advanced applications in semantic retrieval.

Document Embedding with Paragraph Vectors: A Comprehensive Evaluation

The paper "Document Embedding with Paragraph Vectors," authored by Andrew M. Dai, Christopher Olah, and Quoc V. Le, presents an in-depth exploration and evaluation of the Paragraph Vector approach for learning distributed representations of texts. The focus of this research is to benchmark the Paragraph Vector technique against traditional methods such as Latent Dirichlet Allocation (LDA) and bag-of-words models across different text similarity tasks, using datasets like Wikipedia and arXiv.

Introduction and Background

The quest for effective text representation is pivotal in improving machine understanding of language. Historically, approaches such as bag-of-words and n-gram models have been foundational, albeit limited in capturing contextual semantic information. More advanced techniques like LDA offer keyword and topic modeling but still fall short on capturing complex semantic structures. The emergence of distributed representations for words and, by extension, documents has opened new avenues. The Paragraph Vector method, as introduced by Le and Mikolov, enables documents to be represented in dense vector forms which encapsulate their semantic properties.

Methodology

The study examines both the "Distributed Memory" and "Distributed Bag of Words" models of Paragraph Vectors but emphasizes the latter due to its computational efficiency. The researchers illuminate the architecture whereby paragraph vectors are either concatenated or averaged with local context word vectors to predict subsequent words, thus embedding contextual document understanding within the vector space.

Experimental Evaluation

The experiments utilize large corpora from Wikipedia and arXiv, with the aim to validate the capability of Paragraph Vectors to capture document semantics. Both qualitative and quantitative evaluations were conducted:

1. Wikipedia Similarity Tasks: Paragraph Vectors were trained on Wikipedia articles, demonstrating superior abilities in clustering semantically similar documents when compared to LDA. The experiments involving triplet evaluations provided a quantitative edge with an accuracy peak of 93.0% at an embedding dimensionality of 10,000.
2. arXiv Article Similarity: Document embeddings were similarly validated on arXiv papers, where Paragraph Vectors performed comparably with LDA, but exhibited less sensitivity to the dimensionality of the embeddings. Notably, their robustness across various embedding sizes shows potential for broad applicability in text-based retrieval tasks.
3. Vector Operations: A compelling facet of the research is the application of vector arithmetic to Paragraph Vectors, drawing analogies with word vector operations. Such operations demonstrated potential in generating related content vectors, applicable in tasks like analogy generation and similarity querying across languages and domains.

Results and Discussion

The Paragraph Vectors outperformed LDA in the Wikipedia dataset experiments, showcasing their efficiency in capturing document semantics beyond mere topic modeling. The consistency in results across both Wikipedia and arXiv corpora highlights the versatility of Paragraph Vectors in diverse text domains. Furthermore, the paper emphasizes a methodological enhancement wherein jointly training word vectors with paragraph vectors improves embedding quality.

Implications and Future Directions

This research underscores the efficacy of Paragraph Vectors in facilitating semantic document analysis and retrieval. The ability to perform vector arithmetic opens new pathways for sophisticated text manipulation and understanding, offering potential advancements in applications such as information retrieval, recommendation systems, and AI-driven content analysis. Future work may focus on scaling these methods to even larger datasets, embedding additional textual features, and refining vector operations to enhance cross-domain applicability.

In summary, this study provides a comprehensive validation of Paragraph Vectors as a potent tool in the arsenal of document representation techniques, rationalizing their use over traditional models in various unsupervised learning contexts.