PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks (1508.00200v1)

Abstract: Unsupervised text embedding methods, such as Skip-gram and Paragraph Vector, have been attracting increasing attention due to their simplicity, scalability, and effectiveness. However, comparing to sophisticated deep learning architectures such as convolutional neural networks, these methods usually yield inferior results when applied to particular machine learning tasks. One possible reason is that these text embedding methods learn the representation of text in a fully unsupervised way, without leveraging the labeled information available for the task. Although the low dimensional representations learned are applicable to many different tasks, they are not particularly tuned for any task. In this paper, we fill this gap by proposing a semi-supervised representation learning method for text data, which we call the \textit{predictive text embedding} (PTE). Predictive text embedding utilizes both labeled and unlabeled data to learn the embedding of text. The labeled information and different levels of word co-occurrence information are first represented as a large-scale heterogeneous text network, which is then embedded into a low dimensional space through a principled and efficient algorithm. This low dimensional embedding not only preserves the semantic closeness of words and documents, but also has a strong predictive power for the particular task. Compared to recent supervised approaches based on convolutional neural networks, predictive text embedding is comparable or more effective, much more efficient, and has fewer parameters to tune.

• The paper introduces a novel semi-supervised predictive text embedding approach that leverages heterogeneous text networks to combine labeled and unlabeled data.
• The method models word-word, word-document, and word-label relationships to preserve second-order proximity, yielding competitive classification scores.
• Results on diverse datasets demonstrate improved micro-F1 scores and computational efficiency compared to conventional unsupervised and supervised models.

Overview of "PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks"

The paper "PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks" introduces a novel methodology for learning text embeddings that leverage both labeled and unlabeled data, aimed at enhancing performance on specific text classification tasks. The proposed approach, Predictive Text Embedding (PTE), integrates the strengths of unsupervised and semi-supervised learning by considering different levels of word co-occurrence information in a unified framework.

Problem Statement

Traditional unsupervised text embedding methods, such as Skip-gram and Paragraph Vector, are powerful yet not optimally tuned for specific predictive tasks due to their unsupervised nature. In contrast, supervised approaches like Convolutional Neural Networks (CNNs) utilize labeled data but often require extensive computational resources and large datasets. The paper addresses the need for a scalable, efficient semi-supervised approach that can leverage both labeled and unlabeled data to produce embeddings with robust predictive power.

Methodology

The PTE method constructs a heterogeneous text network from text data. This network consists of three types of bipartite sub-networks:

1. Word-Word Co-occurrence Network: Captures local context-level word co-occurrences.
2. Word-Document Network: Encodes document-level word co-occurrences.
3. Word-Label Network: Represents class-level word co-occurrences by linking words with category labels.

Each sub-network is modeled to preserve the second-order proximity of nodes (words, documents, labels) within the network, ensuring that similar words (and their compositions) are embedded closely. The embedding vectors are learned jointly by minimizing an objective function that combines the contributions from all sub-networks, thereby effectively integrating the labeled and unlabeled information.

Experimental Setup

The performance of PTE was evaluated on a range of text classification tasks using both long and short documents:

• Long Documents: 20newsgroup, Wikipedia articles, IMDB reviews, and subsets of the RCV1 dataset.
• Short Documents: Titles from DBLP, movie reviews (MR), and tweets (Twitter).

Results

The numerical results demonstrate the effectiveness of PTE:

• On long documents, PTE outperformed both unsupervised methods (e.g., Skip-gram, PVDBOW) and the supervised CNN model. For instance, PTE yielded higher micro-F1 and macro-F1 scores on datasets like 20newsgroup (Micro-F1: 84.20), Wikipedia (Micro-F1: 82.51), and IMDB (Micro-F1: 89.80).
• On short documents, PTE was competitive and, in certain cases, superior to CNN. The PTE(joint) method particularly showed improved performance when the size of labeled data was abundant.
• The results on varying labeled and unlabeled data indicated that PTE benefits significantly from the integration of both data sources, showcasing robust performance improvements compared to other methods.

Implications and Future Work

The PTE methodology presents significant implications for both theoretical and practical applications:

• Scalability and Efficiency: PTE is efficient, scales well with large datasets, and requires fewer parameters to tune compared to deep learning models like CNNs.
• Integration of Labeled and Unlabeled Data: The ability to jointly train with both types of data highlights a practical advancement in semi-supervised learning approaches.
• Versatility Across Document Lengths: PTE handles both long and short documents effectively, making it versatile for varied text classification scenarios.

Conclusion

The "PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks" paper provides a compelling alternative to traditional text embedding techniques by effectively marrying the strengths of unsupervised learning with supervised information within a semi-supervised framework. This approach demonstrates superior or comparable performance to state-of-the-art methods while being computationally efficient, indicating promising future directions for developments in semi-supervised learning and text classification. Additional improvements could potentially involve leveraging word order information to further refine embeddings, particularly beneficial for tasks involving short text data.