Complete dataset card with domains, entity types, CLS labels, usage, citation

README.md

@@ -4,6 +4,7 @@ language:
 license: cc-by-sa-4.0
 task_categories:
   - token-classification
+  - text-classification
 task_ids:
   - named-entity-recognition
   - part-of-speech
@@ -12,23 +13,28 @@ tags:
   - ner
   - pos
   - dependency-parsing
+  - dialog-act
   - multi-task
   - conllu
+  - spacy
+  - gpt-validated
 size_categories:
   - 100K<n<1M
 ---
 
 # kniv-corpus-en
 
-Multi-task NLP corpus for English: NER + POS tagging + dependency parsing annotations.
-
-Built for training [uniko](https://github.com/rustic-ai/uniko) NLP models.
+Multi-domain NLP corpus for English with annotations for NER, POS tagging, dependency parsing, and sentence classification (dialog act). Built for training [kniv](https://github.com/rustic-ai/kniv-nlp-models) multi-task NLP models for the [uniko](https://github.com/rustic-ai/uniko) cognitive memory system.
 
 ## Dataset Details
 
-- **Format:** CoNLL-U with NER annotations in MISC field
-- **Annotation:** spaCy `en_core_web_trf` + GPT validation
-- **Total sentences:** 785,722
+| | |
+|---|---|
+| **Format** | CoNLL-U with NER and CLS annotations in metadata/MISC fields |
+| **Sentences** | 785,722 |
+| **Annotation** | spaCy `en_core_web_trf` + GPT-5.4 validation corrections |
+| **CLS labels** | GPT-5.4-nano (9 dialog act types) |
+| **License** | CC BY-SA 4.0 |
 
 ### Splits
 
@@ -40,26 +46,83 @@ Built for training [uniko](https://github.com/rustic-ai/uniko) NLP models.
 
 ### Domains
 
-| Domain |
-|--------|
-| conversation |
-| narrative |
-| business |
-| technical |
-| news |
-| encyclopedic |
-
-### Annotation Fields
-
-Each token has:
-- `ID` — token index (1-based)
-- `FORM` — word form
-- `LEMMA` — lemma
-- `UPOS` — Universal POS tag
-- `XPOS` — language-specific POS tag
-- `HEAD` — head token index
-- `DEPREL` — dependency relation
-- `MISC` — NER tags in BIO format (e.g., `NER=B-ORG`)
+| Domain | Sentences | Sources | License |
+|--------|-----------|---------|---------|
+| **Conversation** | 218,343 | Taskmaster 1/2/3, OASST1, MultiWOZ 2.2, Glaive Function Calling, Discord Dialogues | CC BY-4.0 / Apache 2.0 / MIT |
+| **Business** | 197,891 | SEC EDGAR 10-K, Enron emails, OpenStax textbooks, Odoo docs, Wikipedia, CUAD contracts, OpenAlex abstracts | Public domain / CC BY-4.0 / CC BY-SA 3.0 / ODC-BY |
+| **Technical** | 193,032 | Wikipedia (CS/engineering), Python documentation | CC BY-SA 3.0 / PSF License |
+| **Encyclopedic** | 66,490 | Wikipedia (science, history, geography, arts) | CC BY-SA 3.0 |
+| **News** | 58,026 | Wikinews, Wikipedia (journalism, politics) | CC BY 2.5 / CC BY-SA 3.0 |
+| **Narrative** | 51,940 | Project Gutenberg (classic fiction) | Public domain |
+
+### Annotation Pipeline
+
+1. **Collect** — raw text from open sources (APIs, HuggingFace datasets, git repos)
+2. **Preprocess** — sentence splitting, cleaning, quality filtering, deduplication
+3. **Annotate** — spaCy `en_core_web_trf` produces POS, NER, dependency parse per sentence
+4. **Validate** — GPT-5.4-nano checks annotations, returns corrections for POS/NER/dep errors
+5. **Classify** — GPT-5.4-nano assigns dialog act labels (9 types)
+6. **Export** — corrections applied, CLS labels embedded, split into train/dev/test
+
+### NER Entity Types (18)
+
+BIO-tagged in the MISC column:
+
+| Type | Description | Example |
+|------|-------------|---------|
+| PERSON | People | *Caroline*, *Dr. Smith* |
+| ORG | Organizations | *Apple*, *United Nations* |
+| GPE | Geopolitical entities | *France*, *New York* |
+| LOC | Locations | *Mount Everest*, *Pacific Ocean* |
+| DATE | Dates and periods | *January 2024*, *last quarter* |
+| TIME | Times | *3pm*, *two hours* |
+| MONEY | Monetary values | *$394B*, *50 million euros* |
+| PERCENT | Percentages | *15.3%*, *a third* |
+| QUANTITY | Measurements | *100 kilometers*, *5 pounds* |
+| ORDINAL | Ordinal numbers | *first*, *3rd* |
+| CARDINAL | Cardinal numbers | *three*, *42* |
+| NORP | Nationalities/groups | *British*, *Republican* |
+| FAC | Facilities | *the White House*, *Highway 101* |
+| PRODUCT | Products | *iPhone*, *Boeing 747* |
+| EVENT | Events | *World War II*, *Olympics* |
+| WORK_OF_ART | Works of art | *Hamlet*, *The Starry Night* |
+| LAW | Laws | *the First Amendment* |
+| LANGUAGE | Languages | *English*, *Mandarin* |
+
+### CLS Dialog Act Labels (9)
+
+Embedded as `# cls = <label>` comment in each sentence:
+
+| Label | Action in uniko | Example |
+|-------|----------------|---------|
+| `inform` | Extract observation | *"Revenue grew 15% last quarter"* |
+| `correction` | Flag knowledge for update | *"No, it was actually Tuesday"* |
+| `agreement` | Reinforce observation | *"Yes, exactly"* |
+| `question` | Record knowledge gap | *"Where is the report?"* |
+| `plan_commit` | Link to Goal/Task | *"I'll send it tomorrow"* |
+| `request` | Create action node | *"Show me the data"* |
+| `feedback` | Skip extraction | *"OK"*, *"Got it"* |
+| `social` | Skip extraction | *"Hi"*, *"Thanks"* |
+| `filler` | Skip entirely | *"Um"*, *"Well"* |
+
+### CoNLL-U Format
+
+Each sentence has metadata comments followed by token rows:
+
+```
+# sent_id = business-000042
+# text = Apple reported $394B revenue in Q4.
+# cls = inform
+1    Apple      apple    PROPN    NNP    _    2    nsubj     _    NER=B-ORG
+2    reported   report   VERB     VBD    _    0    root      _    _
+3    $394B      $394b    NUM      CD     _    2    obj       _    NER=B-MONEY
+4    revenue    revenue  NOUN     NN     _    3    appos     _    _
+5    in         in       ADP      IN     _    6    case      _    _
+6    Q4         q4       PROPN    NNP    _    2    obl       _    NER=B-DATE
+7    .          .        PUNCT    .      _    2    punct     _    _
+```
+
+Columns: ID, FORM, LEMMA, UPOS, XPOS, FEATS, HEAD, DEPREL, DEPS, MISC
 
 ## Usage
 
@@ -74,9 +137,16 @@ with open(path) as f:
     sentences = conllu.parse(f.read())
 
 for sent in sentences[:3]:
-    print(sent.metadata["text"])
+    # Metadata
+    print(f"Text: {sent.metadata['text']}")
+    print(f"CLS:  {sent.metadata.get('cls', 'N/A')}")
+
+    # Tokens
     for token in sent:
-        print(f"  {token['form']:20s} POS={token['upos']:6s} DEP={token['deprel']}")
+        ner = token.get("misc", {})
+        ner_tag = ner.get("NER", "O") if isinstance(ner, dict) else "O"
+        print(f"  {token['form']:20s} POS={token['upos']:6s} DEP={token['deprel']:12s} NER={ner_tag}")
+    print()
 ```
 
 ## Source
@@ -84,6 +154,18 @@ for sent in sentences[:3]:
 - **Training code:** [rustic-ai/kniv-nlp-models](https://github.com/rustic-ai/kniv-nlp-models)
 - **Trained models:** [dragonscale-ai on HuggingFace](https://huggingface.co/dragonscale-ai)
 
+## Citation
+
+```bibtex
+@misc{kniv-corpus-en,
+  title={kniv-corpus-en: Multi-domain NLP Corpus for English},
+  author={Dragonscale AI},
+  year={2026},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/dragonscale-ai/kniv-corpus-en}
+}
+```
+
 ## License
 
-CC BY-SA 4.0. See individual domain READMEs in the source repo for per-source licenses.
+CC BY-SA 4.0. Individual domain source licenses are listed in the domains table above and in the [source repository](https://github.com/rustic-ai/kniv-nlp-models/tree/main/corpus/domains).