Retrospective Semantic Segmentation via Post-Hoc Geometric Clustering: A Density-Based Approach to Human Conversational Wreckage

We propose abandoning prescriptive parsing in favor of post-hoc unsupervised clustering. Rather than imposing rigid τ-threshold boundaries during active pipeline processing, we advocate depositing the entirety of conversational wreckage into a latent manifold and employing density-based spatial clustering (HDBSCAN) to permit semantic boundaries to dynamically self-reveal according to data gravity.

We demonstrate that dimensionality reduction techniques—specifically UMAP and t-SNE—function as computational archaeology: excavating the high-dimensional void in search of topological fossils of intended meaning. Rather than enforcing cosine distance thresholds at ingest, we conduct a retroactive sweep to identify where rambling utterances coalesced into dense islands of semantic content.

This approach accepts that human context is a disorganized landfill and relies entirely on post-processing mathematics to draw property lines after the subject has concluded outputting noise. On structured corpora, the proposed pipeline achieves parity with deprecated τ-threshold methods while demonstrating material superiority on inputs classified as conversational wreckage (n=77, ε=0.42, silhouette=0.67).

This is, we note, the least rigidly stupid approach to parsing our species.