---
language:
  - en
license: cc-by-sa-4.0
task_categories:
  - token-classification
  - text-classification
task_ids:
  - named-entity-recognition
  - part-of-speech
  - parsing
tags:
  - ner
  - pos
  - dependency-parsing
  - dialog-act
  - multi-task
  - conllu
  - spacy
  - gpt-validated
  - llm-gold-filtered
size_categories:
  - 100K<n<1M
---

# kniv-corpus-en

A multi-domain English NLP corpus with four annotation layers: Named Entity Recognition (18 types), POS tagging (17 UPOS tags), dependency parsing, and dialog act classification (9 types). All data is commercially licensed (CC BY-SA 4.0 compatible).

Built for training [kniv](https://github.com/rustic-ai/kniv-nlp-models) multi-task NLP models that power the [uniko](https://github.com/rustic-ai/uniko) cognitive memory system.

## Quick Start

```python
from datasets import load_dataset

# Load the full corpus via HuggingFace
ds = load_dataset("dragonscale-ai/kniv-corpus-en")
print(ds["train"][0])
```

```python
# Load gold-filtered corpus (recommended for training)
import pandas as pd
from huggingface_hub import hf_hub_download

path = hf_hub_download("dragonscale-ai/kniv-corpus-en", "corpus/gold/train.parquet", repo_type="dataset")
gold = pd.read_parquet(path)
print(f"{len(gold)} gold sentences")
```

## Repository Structure

```
dragonscale-ai/kniv-corpus-en/
|
+-- data/                                   # Full corpus (HF auto-detected Parquet)
|   +-- train-00000-of-00001.parquet            525,850 sentences  (127.7 MB)
|   +-- dev-00000-of-00001.parquet               65,731 sentences  (13.8 MB)
|   +-- test-00000-of-00001.parquet              65,732 sentences  (14.0 MB)
|
+-- corpus/gold/                            # Gold-filtered corpus (recommended)
|   +-- train.parquet                           237,646 sentences  (56.8 MB)
|   +-- dev.parquet                              65,731 sentences  (13.8 MB)
|   +-- test.parquet                             65,732 sentences  (14.0 MB)
|   +-- progress.json                           Filtering metadata
|
+-- prepared/deberta-v3-large-nlp-en/       # Training-ready JSON splits
|   +-- ner_train.json                          45,000 examples (domain-balanced subsample)
|   +-- ner_dev.json                            65,731 examples
|   +-- ner_test.json                           65,732 examples
|   +-- ud_train.json                           12,544 examples (UD English EWT)
|   +-- ud_dev.json                              2,001 examples
|   +-- ud_test.json                             2,077 examples
|   +-- label_vocabs.json                       Label vocabularies for all tasks
|
+-- train.conllu                            # Full corpus in CoNLL-U format (547.7 MB)
+-- dev.conllu                              (67.9 MB)
+-- test.conllu                             (68.8 MB)
+-- metadata.json                           Split sizes and domain list
+-- README.md
```

## How the Data Was Built

### Collection

Text was collected from 17 open sources across 6 domains, chosen for domain diversity and commercial licensing:

| Domain | Sentences | Sources |
|--------|-----------|---------|
| **Business** | 197,891 | SEC EDGAR 10-K filings (public domain), Enron email corpus (public domain), OpenStax business textbooks (CC BY-4.0), Odoo ERP documentation (CC BY-SA 3.0), Wikipedia business articles (CC BY-SA 3.0), CUAD contract passages (CC BY-4.0), OpenAlex abstracts (ODC-BY) |
| **Technical** | 193,032 | Wikipedia CS/engineering articles (CC BY-SA 3.0), Python official documentation (PSF License) |
| **Conversation** | 89,934 | Taskmaster 1/2/3 (CC BY-4.0), OpenAssistant OASST1 (Apache 2.0), MultiWOZ 2.2 (Apache 2.0), Glaive Function Calling (Apache 2.0), Discord Dialogues (Apache 2.0) |
| **Encyclopedic** | 66,490 | Wikipedia articles on science, history, geography, arts (CC BY-SA 3.0) |
| **News** | 58,026 | Wikinews articles (CC BY-SA 2.5), Wikipedia journalism/politics articles (CC BY-SA 3.0) |
| **Narrative** | 51,940 | Project Gutenberg classic fiction (public domain) |

### Preprocessing

- **Sentence splitting** (non-conversation): spaCy sentencizer splits documents into individual sentences
- **Utterance extraction** (conversation): each dialog turn kept as a single unit (no sentence splitting -- conversational utterances are natural dialog act boundaries)
- **Conversational context**: within each conversation, `prev_text` links each utterance to the preceding turn for context-dependent classification
- **Deduplication**: exact-match dedup by text content; conversation domain deduped by `(conv_id, turn_idx)` pairs
- **Quality filtering**: minimum length, encoding validation, boilerplate removal

### Annotation

Four-layer annotation in a single pipeline pass:

1. **NER + POS + Dependency**: spaCy `en_core_web_trf` (transformer-based) annotates all three in one forward pass, producing CoNLL-U + JSONL output
2. **Validation**: GPT-5.4-nano reviews annotations in batches of 5 sentences, returning corrections for POS, NER, and dependency labels. Only valid UPOS corrections are accepted (invalid suggestions like "PART (acceptable)" are rejected). Orphan BIO I-tags (I- without preceding B-) are automatically repaired to B-.
3. **Classification**: GPT-5.4-nano assigns one of 9 dialog act labels per sentence in batches of 20. Conversation domain sentences include the previous utterance (`prev_text`) as context for labels like `correction`, `agreement`, and `filler`.

### Gold Filtering

The full corpus (525,850 train sentences) was validated sentence-by-sentence using **Qwen3-8B** via vLLM on an A100 GPU with 64 concurrent requests:

| Check | What | Rejection Rate |
|-------|------|----------------|
| **NER** | Every entity span: "Is '{entity}' correctly tagged as {type}?" | 9.3% |
| **CLS** | Dialog act label validated (with prev_text context for conversations) | 4.0% |
| **POS** | Up to 8 POS tags per sentence validated against Universal POS tagset | 40.9% |

Sentences failing any check were removed. **237,646 sentences survived** (45.2% of training data). The high POS rejection rate reflects the LLM checking 8 tokens per sentence -- even a ~5% per-token disagreement rate compounds to ~34% per-sentence rejection.

### Training Data Preparation

The `prepared/` directory contains training-ready JSON splits:

- **NER data** (45,000 examples): domain-balanced subsample from the 237K gold corpus, with targets: conversation 15K, business 10K, technical 7K, narrative 5K, news 5K, encyclopedic 3K
- **UD data** (12,544 examples): Universal Dependencies English EWT v2.14 (expert-annotated, not gold-filtered -- already gold standard)

## Data Format

### Parquet Columns

| Column | Type | Description |
|--------|------|-------------|
| `sent_id` | string | Unique ID: `{domain}-{index}` |
| `text` | string | Sentence text |
| `prev_text` | string? | Previous utterance (conversation domain, nullable) |
| `cls` | string | Dialog act label |
| `tokens` | list[str] | Tokenized words |
| `pos_tags` | list[str] | Universal POS tags |
| `heads` | list[int] | Dependency head indices (0 = root) |
| `deprels` | list[str] | Dependency relation labels |
| `ner_tags` | list[str] | BIO-format NER tags |

### CoNLL-U Format

Standard 10-column CoNLL-U with metadata comments:

```
# sent_id = conversation-004217
# text = Oh, could you please change it for outside?
# prev_text = It looks like the table will be for inside.
# cls = correction
1    Oh         oh         INTJ     UH     _    5    discourse    _    _
2    ,          ,          PUNCT    ,      _    5    punct        _    _
3    could      could      AUX      MD     _    5    aux          _    _
4    you        you        PRON     PRP    _    5    nsubj        _    _
5    please     please     VERB     VB     _    0    root         _    _
6    change     change     VERB     VB     _    5    xcomp        _    _
7    it         it         PRON     PRP    _    6    obj          _    _
8    for        for        ADP      IN     _    9    case         _    _
9    outside    outside    NOUN     NN     _    6    obl          _    _
10   ?          ?          PUNCT    .      _    5    punct        _    _
```

NER tags appear in the MISC column as `NER=B-ORG`, `NER=I-ORG`, etc.

## Annotation Details

### NER Entity Types (18)

| Type | Example |
|------|---------|
| PERSON | *Dr. Smith*, *Caroline* |
| ORG | *Apple*, *United Nations* |
| GPE | *France*, *New York* |
| LOC | *Mount Everest*, *Pacific Ocean* |
| DATE | *January 2024*, *last quarter* |
| TIME | *3pm*, *two hours* |
| MONEY | *$394B*, *50 million euros* |
| PERCENT | *15.3%*, *a third* |
| QUANTITY | *100 kilometers*, *5 pounds* |
| ORDINAL | *first*, *3rd* |
| CARDINAL | *three*, *42* |
| NORP | *British*, *Republican* |
| FAC | *the White House*, *Highway 101* |
| PRODUCT | *iPhone*, *Boeing 747* |
| EVENT | *World War II*, *Olympics* |
| WORK_OF_ART | *Hamlet*, *The Starry Night* |
| LAW | *the First Amendment* |
| LANGUAGE | *English*, *Mandarin* |

### Dialog Act Labels (9)

These labels are designed for uniko's cognitive memory system:

| Label | Description | Uniko Action | Gold Distribution |
|-------|-------------|-------------|-------------------|
| `inform` | States a fact or observation | Extract observation | 73.0% |
| `question` | Asks for information | Record knowledge gap | 6.3% |
| `plan_commit` | Commits to action, offers, suggests | Link to Goal/Task | 5.3% |
| `request` | Asks someone to do something | Create action node | 4.5% |
| `filler` | Fragments, headers, structural text | Skip entirely | 4.4% |
| `correction` | Corrects or contradicts prior statement | Flag for update | 3.2% |
| `social` | Greeting, goodbye, thanks, apology | Skip extraction | 1.3% |
| `agreement` | Agrees with or confirms something | Reinforce observation | 1.2% |
| `feedback` | Acknowledges without adding info | Skip extraction | 0.8% |

Context-dependent labels (`correction`, `agreement`, `feedback`) use `prev_text` for classification in the conversation domain. For non-conversational text, these labels are assigned based on sentence content alone.

### Dependency Parsing (dep2label)

Dependencies are encoded as token-level labels using the [rel-pos scheme](https://aclanthology.org/N19-1077/) (Strzyz et al., 2019):

```
+1@nsubj@VERB   ->  "1st VERB to the right is the head, relation = nsubj"
-2@amod@NOUN    ->  "2nd NOUN to the left is the head, relation = amod"
 0@root@ROOT    ->  "this token is the root"
```

This reformulation enables dependency parsing as standard token classification (~1,411 unique labels). Decoding back to a tree is O(n) per sentence.

## Full vs Gold: Which Should I Use?

| Use Case | Dataset |
|----------|---------|
| Model training | `corpus/gold/` or `prepared/` |
| Corpus linguistics / analysis | `data/` (full corpus) |
| Building custom subsamples | `corpus/gold/` |
| Evaluation | `prepared/*/ner_dev.json` etc. |

The **gold corpus** is a strict subset of the full corpus. Every sentence in gold passed three independent LLM validation checks (NER, CLS, POS). The **prepared data** is a domain-balanced subsample of the gold corpus, ready for model training with label vocabularies.

## Training

```bash
git clone https://github.com/rustic-ai/kniv-nlp-models
cd kniv-nlp-models

# Prepare training data from gold corpus
python models/deberta-v3-large-nlp-en/prepare_data.py \
    --gold corpus/output/final-gold

# Train teacher model (DeBERTa-v3-large, 435M params)
python models/deberta-v3-large-nlp-en/train.py

# Quick test (1 epoch, 100 samples)
python models/deberta-v3-large-nlp-en/train.py --quick-test
```

## Pipeline

```
collect  ->  preprocess  ->  annotate   ->  validate   ->  classify   ->  export  ->  gold filter
(17 src)     (dedup,         (spaCy         (GPT-5.4       (GPT-5.4       (CoNLL-U    (Qwen3-8B
              split,          trf)           nano)          nano)           Parquet)    vLLM)
              prev_text)
657K sentences -----------------------------------------------------------> 237K gold
```

## Source

- **Code**: [rustic-ai/kniv-nlp-models](https://github.com/rustic-ai/kniv-nlp-models)
- **Models**: [dragonscale-ai on HuggingFace](https://huggingface.co/dragonscale-ai)

## Citation

```bibtex
@misc{kniv-corpus-en-2026,
  title={kniv-corpus-en: Multi-domain English NLP Corpus},
  author={Dragonscale AI},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/dragonscale-ai/kniv-corpus-en}
}
```

## License

CC BY-SA 4.0. All source datasets are commercially licensed (CC BY, CC BY-SA, Apache 2.0, MIT, PSF, public domain, or ODC-BY).