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New framework automates taxonomy construction and evaluation at scale

By PulseAugur Editorial · [1 sources] ·

A new framework called TaxCE has been developed for automatically constructing and evaluating hierarchical taxonomies from large volumes of unstructured text data. This framework addresses limitations in existing methods by progressively condensing content, deduplicating semantic units, and organizing topics bottom-up. TaxCE also introduces three novel evaluation metrics—Exclusivity, Exhaustivity, and Granularity (EEG)—which are integrated into an iterative refinement process to improve taxonomy quality. Experiments show TaxCE significantly outperforms current topic modeling, neural, and LLM-based approaches, with human evaluations confirming its superior actionability and navigability. AI

IMPACT This framework could significantly improve how large organizations structure and analyze user feedback, leading to more actionable insights.

RANK_REASON The cluster contains a research paper detailing a new framework and evaluation metrics for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework automates taxonomy construction and evaluation at scale

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The cluster contains a research paper detailing a new framework and evaluation metrics for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Sandeep Sricharan Mukku, Albert Aristotle Nanda, Rohit Pyati ·

    TaxCE : A Framework for Automated Taxonomy Construction and Evaluation at Scale

    arXiv:2608.30614v1 Announce Type: new Abstract: Organizing unstructured feedback text into hierarchical taxonomy is a fundamental challenge in NLP, particularly in domains where feedback arrives at massive scale in varied forms such as reviews, transcripts, and surveys. Existing …

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