unit-classification

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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).

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

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# Unit Classification Single-layer per-unit classification against a fixed, parameterized label set — no cross-unit or document-level dependency. Covers 7 methods (AZ/CoreSC/PubMed-RCT/NICTA-PIBOSO/CSAbstruct/CODA-19/Swales) plus TDMS's tuple-output variant, plus CSFCube's 3 facet labels as one more label_set option. ## Execution Subagent — spawned via spawn-agent skill. ## Why SciERC/SciREX/NCG Are NOT Parameterized Here An earlier graph draft tried to fold SciERC/SciREX into this node via a boolean toggle; the coverage audit (S6) found this doesn't work — those methods need document-level coreference clustering and (for SciREX) saliency judgment over ALL mentions in the paper, not per-unit independent classification. A boolean can't absorb that difference; they live in `multi-stage-cascade-extraction` instead. ## CSFCube's Role Here `csfcube-facet` is documented as out-of-scope as its own SOP (its real task — multi-document pairwise relevance ranking — has no single-paper analog), but its 3 facet-label definitions (Background/Objective, Method, Result) are reused here as one more valid `label_set` option, per spec §3. <!-- 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) -->

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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

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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 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 Featured

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.

392 Updated 1 weeks ago
yogsoth-ai