Self-Taught Convolutional Neural Networks for Short Text Clustering (1701.00185v1)

Abstract: Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC^2), which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep text representation in an unsupervised manner. In our framework, the original raw text features are firstly embedded into compact binary codes by using one existing unsupervised dimensionality reduction methods. Then, word embeddings are explored and fed into convolutional neural networks to learn deep feature representations, meanwhile the output units are used to fit the pre-trained binary codes in the training process. Finally, we get the optimal clusters by employing K-means to cluster the learned representations. Extensive experimental results demonstrate that the proposed framework is effective, flexible and outperform several popular clustering methods when tested on three public short text datasets.

• The paper introduces the STC² framework, which uses CNNs with dimensionality reduction to address data sparsity in short text clustering.
• The method leverages unsupervised binary code embedding and K-means, achieving superior performance on public datasets via improved ACC and NMI metrics.
• The study’s findings suggest significant applications in user profiling and recommendation systems, with potential for further enhancement through novel neural architectures.

Analysis of Self-Taught Convolutional Neural Networks for Short Text Clustering

The research paper titled "Self-Taught Convolutional Neural Networks for Short Text Clustering" presents a comprehensive paper on clustering short text data using a novel framework, STC$^2$. The paper introduces a method that leverages Convolutional Neural Networks (CNNs) to learn deep feature representations of text in an unsupervised manner, aiming to address the inherent challenge of data sparsity encountered in short text clustering.

Framework Overview

The STC$^2$ framework is designed to enhance short text clustering by combining CNNs with dimensionality reduction techniques. The pipeline consists of several phases:

1. Binary Code Embedding: Raw text features undergo transformation into compact binary codes using unsupervised dimensionality reduction methods such as Average Embedding (AE), Latent Semantic Analysis (LSA), Laplacian Eigenmaps (LE), and Locality Preserving Indexing (LPI).
2. CNN-Based Representation Learning: Subsequently, these text representations are processed into word embeddings, which feed into a CNN model. The CNN is tasked with learning deep, non-biased feature representations by using its output to fit the pre-trained binary codes.
3. Clustering Procedure: The final representations are clustered using the K-means algorithm, enabling the extraction of semantic groupings within the data.

Experimental Validation

The paper presents empirical results from experiments conducted on three public short text datasets: SearchSnippets, StackOverflow, and Biomedical. These datasets vary in domain and complexity, providing a robust test environment for STC$^2$. Significant enhancements in clustering performance were evidenced by metrics such as accuracy (ACC) and normalized mutual information (NMI).

Crucially, the STC$^2$ framework demonstrated superior performance compared to traditional clustering approaches and several neural network-based methods. The extensive evaluations reflect STC$^2$'s ability to flexibly integrate various semantic features and improve clustering outcomes effectively.

Strong Numerical Results

The experimental results highlight that STC$^2$ substantially outperforms baseline methods across all evaluated datasets. Notably, configurations using Laplacian Eigenmaps (LE) and Locality Preserving Indexing (LPI) as dimensionality reduction techniques achieved standout performance, indicating their effectiveness in capturing semantic richness distinct from word embeddings.

Implications and Future Work

The implications of the paper are twofold. Practically, the ability of STC$^2$ to cluster short texts without extensive pre-processing or reliance on external labels presents significant advantages for applications in user profiling and recommendation systems. Theoretically, the framework's capability to incorporate diverse semantic features highlights an area for potential expansion, inviting exploration of additional semantic extraction techniques.

Future research directions could investigate optimizing semantic feature selection processes and further enhancing STC$^2$ with advancements in unsupervised learning and dimensionality reduction. Moreover, analyzing the effect of varying CNN architectures or integrating novel neural network models could offer further improvements.

In summary, this research provides a valuable contribution to the domain of text clustering by introducing a flexible, effective framework that capitalizes on the strengths of CNNs and dimensionality reduction to address challenges inherent in short text data.