Tag-Weighted Topic Model For Large-scale Semi-Structured Documents

TL;DR

This paper introduces a Tag-Weighted Topic Model (TWTM) that effectively leverages both tags and text in large-scale semi-structured documents, improving modeling accuracy and efficiency.

Contribution

The paper proposes a novel TWTM framework that integrates tags and words for better document modeling and introduces three scalable MapReduce solutions for large datasets.

Findings

TWTM outperforms state-of-the-art models in document modeling, tag prediction, and classification.

The proposed methods are efficient and robust on large-scale SSDs.

Distributed solutions significantly reduce computation time while maintaining accuracy.

Abstract

To date, there have been massive Semi-Structured Documents (SSDs) during the evolution of the Internet. These SSDs contain both unstructured features (e.g., plain text) and metadata (e.g., tags). Most previous works focused on modeling the unstructured text, and recently, some other methods have been proposed to model the unstructured text with specific tags. To build a general model for SSDs remains an important problem in terms of both model fitness and efficiency. We propose a novel method to model the SSDs by a so-called Tag-Weighted Topic Model (TWTM). TWTM is a framework that leverages both the tags and words information, not only to learn the document-topic and topic-word distributions, but also to infer the tag-topic distributions for text mining tasks. We present an efficient variational inference method with an EM algorithm for estimating the model parameters. Meanwhile, we propose three large-scale solutions for our model under the MapReduce distributed computing platform for modeling large-scale SSDs. The experimental results show the effectiveness, efficiency and the robustness by comparing our model with the state-of-the-art methods in document modeling, tags prediction and text classification. We also show the performance of the three distributed solutions in terms of time and accuracy on document modeling.