Towards semantically sensitive text clustering: a feature space modeling technology based on dimension extension.

Area of Science:

• Natural Language Processing
• Data Mining
• Information Retrieval

Background:

• Text clustering aims to group similar documents.
• Existing methods struggle with natural language complexity and diversity.
• Improved semantic sensitivity in clustering is needed.

Purpose of the Study:

• Propose an extension-based feature modeling approach for semantically sensitive text clustering.
• Enhance feature space construction and similarity computation.
• Improve clustering performance by addressing natural language challenges.

Main Methods:

• Developed an extension-based feature modeling approach.
• Constructed a novel feature space incorporating extension features.
• Proposed a similarity computation method combining traditional and extension spaces.
• Enabled organization of clusters with varying granularities.

Main Results:

• Effectively addressed the adverse effects of natural language complexity and diversity.
• Significantly improved the semantic sensitivity of text clustering.
• Demonstrated effectiveness through experimental evaluations on standard algorithms and datasets.
• Validated the proposed approach's performance.

Conclusions:

• The extension-based feature modeling approach enhances semantically sensitive text clustering.
• Combining traditional and extension spaces improves clustering accuracy.
• The method is effective and adaptable for organizing clusters at different granularities.