unit-segmentation

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Split a paper's text into sentence- or clause-level units (with character offsets) for downstream classification, at a caller-specified granularity and scope (full text, abstract-only, or intro-only). Use this as the mandatory first step whenever any sentence/clause-level classification method (Argumentative Zoning, CoreSC, PubMed-RCT, CSAbstruct, Swales move analysis, CODA-19) needs its input pre-segmented — always precedes unit-classification.

AI & Automation 392 stars 34 forks Updated 1 weeks ago Apache-2.0

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Skill Content

# Unit Segmentation Splits text into labeling units (sentence or clause granularity, scoped to full text/abstract/intro) — pure segmentation, no labeling. ## Execution Subagent — spawned via spawn-agent skill. ## Why This Exists As Its Own Step 7 different classification methods (AZ, CoreSC, PubMed-RCT, NICTA-PIBOSO, CSAbstruct, CODA-19, Swales) all need pre-segmented units but disagree on granularity and scope — factoring segmentation out once, parameterized, avoids duplicating this logic inside `unit-classification` seven times over (graph correction L17/L18: the original graph was missing this step entirely, silently assuming pre-segmented input existed). <!-- BEGIN available-tables (generated) --> ## Available SOPs | SOP | When to use | | --- | --- | | spawn-agent | Spawn a customized CC subagent with full MCP tool access. | <!-- END available-tables (generated) -->

Details

Author
yogsoth-ai
Repository
yogsoth-ai/de-anthropocentric-research-engine
Created
6 months ago
Last Updated
1 weeks ago
Language
HTML
License
Apache-2.0

Integrates with

Model Context Protocol · AI

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AI & Automation Listed

unit-segmentation

Split a paper's text into sentence- or clause-level units (with character offsets) for downstream classification, at a caller-specified granularity and scope (full text, abstract-only, or intro-only). Use this as the mandatory first step whenever any sentence/clause-level classification method (Argumentative Zoning, CoreSC, PubMed-RCT, CSAbstruct, Swales move analysis, CODA-19) needs its input pre-segmented — always precedes unit-classification.

1 Updated 1 weeks ago
yogsoth-ai
AI & Automation Listed

unit-classification

Classify each pre-segmented text unit independently against a fixed label set (Argumentative Zoning, CoreSC, PubMed-RCT, Swales move/step, CODA-19, TDMS, or CSFCube's facet labels), single-layer with no cross-unit dependency. Use this after unit-segmentation has split the text, whenever a sentence- or clause-level rhetorical/functional classification is needed; do not use this for methods requiring document-level coreference reasoning (see multi-stage-cascade-extraction instead).

1 Updated 1 weeks ago
yogsoth-ai
AI & Automation Featured

unit-classification

Classify each pre-segmented text unit independently against a fixed label set (Argumentative Zoning, CoreSC, PubMed-RCT, Swales move/step, CODA-19, TDMS, or CSFCube's facet labels), single-layer with no cross-unit dependency. Use this after unit-segmentation has split the text, whenever a sentence- or clause-level rhetorical/functional classification is needed; do not use this for methods requiring document-level coreference reasoning (see multi-stage-cascade-extraction instead).

392 Updated 1 weeks ago
yogsoth-ai