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Run npx get-skill add tomevault-io/nlp-guide in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Nlp Guide free to use?

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Who published Nlp Guide?

tomevault-io (@tomevault-io) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Nlp Guide by tomevault-io — Agent Skill

Name: Nlp Guide
Author: tomevault-io

File contents

NLP Layer Guide

Overview of the NLP layer in narrativegraphs/nlp/.

Pipelines

Pipeline	Purpose
Pipeline	Full narrative graph (triplets + cooccurrences)
CooccurrencePipeline	Simpler cooccurrence-only extraction

Both handle: document ingestion → extraction → mapping → stats calculation.

Extraction Components

Entity Extraction (`entities/`)

EntityExtractor - Abstract base class
SpacyEntityExtractor - NER + noun chunks with length filtering, non-overlapping selection

Triplet Extraction (`triplets/`)

TripletExtractor - Abstract base class
DependencyGraphExtractor - Verb-first traversal; fine-grained boolean flags per relation type; no sentence-length limits
EntityPairExtractor - Entity-pair traversal; declarative PathPattern list; supports compound "verb prep" predicates; guards: max_sentence_length=60, max_entity_distance=10

Cooccurrence Extraction (`tuplets/`)

CooccurrenceExtractor - Abstract base class
ChunkCooccurrenceExtractor - Sentence-windowed cooccurrences (default)
DocumentCooccurrenceExtractor - All entity pairs in document

Mapping (`mapping/`)

Maps surface forms to canonical labels: dict[str, str]

Mapper - Abstract base class
StemmingMapper - Groups by Porter stemmed form
SubgramStemmingMapper - Stemming + subgram matching
SubgramLemmatizationMapper - Lemmatization + subgram matching (default)

Data Models (`common/`)

SpanAnnotation - Text span with offsets (text, start_char, end_char)
AnnotationContext - Context window (text, doc_offset)
Triplet - subj, pred, obj (SpanAnnotation) + optional context
Tuplet - entity_one, entity_two (SpanAnnotation) + optional context

Architecture

Pipeline / CooccurrencePipeline
    │
    ├── TripletExtractor → Triplet
    ├── EntityExtractor → SpanAnnotation
    ├── CooccurrenceExtractor → Tuplet
    └── Mapper → canonical labels

Default Components

Pipeline:

DependencyGraphExtractor (triplets, default; EntityPairDependencyExtractor is an alternative)
ChunkCooccurrenceExtractor (cooccurrences)
SubgramLemmatizationMapper("noun") (entities)
SubgramLemmatizationMapper("verb") (predicates)

CooccurrencePipeline:

SpacyEntityExtractor (entities)
ChunkCooccurrenceExtractor (cooccurrences)
SubgramLemmatizationMapper("noun") (entities)

Converted and distributed by TomeVault — claim your Tome and manage your conversions.

---name: nlp-guidedescription: Overview of the NLP layer for text extraction and mapping Use when this capability is needed.---# NLP Layer GuideOverview of the NLP layer in `narrativegraphs/nlp/`.## Pipelines| Pipeline | Purpose || ------------------------ | ----------------------------------------------- || **Pipeline** | Full narrative graph (triplets + cooccurrences) || **CooccurrencePipeline** | Simpler cooccurrence-only extraction |Both handle: document ingestion → extraction → mapping → stats calculation.## Extraction Components### Entity Extraction (`entities/`)- **EntityExtractor** - Abstract base class- **SpacyEntityExtractor** - NER + noun chunks with length filtering, non-overlapping selection### Triplet Extraction (`triplets/`)- **TripletExtractor** - Abstract base class- **DependencyGraphExtractor** - Verb-first traversal; fine-grained boolean flags per relation type; no sentence-length limits- **EntityPairExtractor** - Entity-pair traversal; declarative `PathPattern` list; supports compound "verb prep" predicates; guards: `max_sentence_length=60`, `max_entity_distance=10`### Cooccurrence Extraction (`tuplets/`)- **CooccurrenceExtractor** - Abstract base class- **ChunkCooccurrenceExtractor** - Sentence-windowed cooccurrences (default)- **DocumentCooccurrenceExtractor** - All entity pairs in document## Mapping (`mapping/`)Maps surface forms to canonical labels: `dict[str, str]`- **Mapper** - Abstract base class- **StemmingMapper** - Groups by Porter stemmed form- **SubgramStemmingMapper** - Stemming + subgram matching- **SubgramLemmatizationMapper** - Lemmatization + subgram matching (default)## Data Models (`common/`)- **SpanAnnotation** - Text span with offsets (`text`, `start_char`, `end_char`)- **AnnotationContext** - Context window (`text`, `doc_offset`)- **Triplet** - `subj`, `pred`, `obj` (SpanAnnotation) + optional `context`- **Tuplet** - `entity_one`, `entity_two` (SpanAnnotation) + optional `context`## Architecture```Pipeline / CooccurrencePipeline │ ├── TripletExtractor → Triplet ├── EntityExtractor → SpanAnnotation ├── CooccurrenceExtractor → Tuplet └── Mapper → canonical labels```## Default Components**Pipeline:**- `DependencyGraphExtractor` (triplets, default; `EntityPairDependencyExtractor` is an alternative)- `ChunkCooccurrenceExtractor` (cooccurrences)- `SubgramLemmatizationMapper("noun")` (entities)- `SubgramLemmatizationMapper("verb")` (predicates)**CooccurrencePipeline:**- `SpacyEntityExtractor` (entities)- `ChunkCooccurrenceExtractor` (cooccurrences)- `SubgramLemmatizationMapper("noun")` (entities)---> Converted and distributed by [TomeVault](https://tomevault.io/claim/kasperfyhn) — claim your Tome and manage your conversions.

Frequently asked questions

Nlp Guide