Text-as-Signal: Quantitative Semantic Scoring with Embeddings, Logprobs, and Noise Reduction

2026-02-18 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, long

Summary

A new practical pipeline, "Text-as-Signal," converts text corpora into quantitative semantic signals for AI engineering tasks. This workflow represents each news item as a full-document embedding using Qwen2.5 8B Instruct, scores it via logprob-based evaluation over a configurable positional dictionary, and projects it onto a noise-reduced low-dimensional manifold using UMAP for structural interpretation. The dictionary, in a case study, comprised six semantic dimensions applied to 11,922 Portuguese news articles about Artificial Intelligence. The resulting identity space supports both document-level semantic positioning and corpus-level characterization through aggregated profiles. The pipeline integrates Qwen embeddings, UMAP, semantic indicators from the model output space, and a three-stage anomaly-detection procedure, making it suitable for corpus inspection, monitoring, and downstream analytical support, with adaptability to different analytical streams.

Key takeaway

For AI Engineers and Research Scientists building data pipelines, this "Text-as-Signal" workflow offers a robust method to operationalize text data. By generating continuous semantic identities for documents and characterizing corpora through aggregated profiles, you can enhance automated monitoring, improve corpus inspection, and provide richer inputs for downstream analytical and learning tasks, reducing reliance on manual annotations for exploratory analysis.

Key insights

Text can be transformed into operational, continuous semantic data points for AI engineering via embeddings and logprob scoring.

Principles

• LLM weights are a compressed topology of human language.
• Semantic signals are extracted directly from model output space.
• Noise reduction stabilizes semantic maps for interpretation.

Method

The pipeline embeds documents with Qwen2.5, reduces dimensionality with UMAP, applies K-Means clustering, and scores semantic indicators via logprob-based zero-shot evaluation, followed by a three-stage anomaly detection for noise reduction.

In practice

• Use Qwen2.5 8B Instruct for document embeddings.
• Apply UMAP for 5D latent representation and 2D visualization.
• Implement 3-stage anomaly detection for noise reduction.

Topics

• Text-as-Signal
• Quantitative Semantic Scoring
• Document Embeddings
• Logprob-based Evaluation
• Noise Reduction

Best for: NLP Engineer, AI Scientist, Research Scientist, AI Engineer, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.