---
pretty_name: Applied NER Stage 2 - Expanded
language:
- en
task_categories:
- token-classification
tags:
- named-entity-recognition
- ner
license: other
---

# Applied NER Stage 2 — Expanded

An 800-record English NER dataset combining 98 cleaned starter records with 702 policy-compatible records selected from Few-NERD and enriched with deterministic high-precision TIMEDATE, AMOUNT, and JOB annotations.

## Dataset structure

- Repository ID: THemidli/applied-ner-stage2-expanded
- Splits: train (641), test (159)
- Fields: unique_index, source_text, privacy_mask
- Total spans: 4,836
- Exact duplicate texts or IDs: 0
- Train/test normalized-text leakage: 0

| Label | Spans |
|---|---:|
| AMOUNT | 460 |
| JOB | 413 |
| LOCATION | 619 |
| ORGANIZATION | 627 |
| PERSON | 912 |
| PRODUCT | 557 |
| TIMEDATE | 750 |
| WORKOFART | 498 |

## Selection and validation

The pipeline scanned 20,000 Few-NERD candidates from each source split. After policy filters, 16,540 train and 16,576 test candidates were eligible. A deterministic seed (20260802) and greedy label-deficit score selected 562 train and 140 test records. The cleaned seed was split deterministically by original ID (79 train, 19 test). Selection balanced label presence, text length, entity density, and split/source provenance. A label-stratified local-Qwen audit sampled 48 of the 702 imports; all 48 passed after a year-range rule fix. This audit is a second opinion, not ground truth.

## Source and license notes

- Cleaned starter portion: [polygraf-ai/applied-nlp-ner-candidate-starter-100](https://huggingface.co/datasets/polygraf-ai/applied-nlp-ner-candidate-starter-100); no general license is stated. Publication is required by the task, but broader reuse may require permission.
- Few-NERD portion: [DFKI-SLT/few-nerd](https://huggingface.co/datasets/DFKI-SLT/few-nerd), licensed CC BY-SA 4.0. Original project: [thunlp/Few-NERD](https://github.com/thunlp/Few-NERD).
- Metadata uses license: other because the combined terms are mixed/partly unstated. The Few-NERD-derived portion remains subject to CC BY-SA 4.0.

## Labels

- **PERSON** — A named person, including given names and surnames.
- **ORGANIZATION** — A named company or institution.
- **LOCATION** — A named place, including a city, country, region, street, landmark, or geographic area.
- **TIMEDATE** — An expression that places something on a timeline, including dates, clock times, and durations used as time.
- **PRODUCT** — A named commercial product, device, or branded good.
- **WORKOFART** — A named creative or published work, such as a book, film, song, or titled publication.
- **JOB** — An occupational title or formal work role when it functions as such in context.
- **AMOUNT** — A measurable or countable quantity that is not a time or date expression.

## Baseline labeling rules (fixed)

- A labeled mention covers the entity itself, not surrounding grammar.
- Function words stay outside unless they are truly part of the proper name.
- Bare type words and category descriptors are not labeled on their own.
- A multi-word name is one span; separate entities are labeled separately.
- Coordinated names are separate spans unless the conjunction belongs to an established name.
- Ordinary punctuation and stray whitespace stay outside unless they belong to the name.
- Possessive markers stay outside unless the full possessive form is the name.
- Labels follow context, not surface form alone.
- Quantities used as time are TIMEDATE, not AMOUNT.

## Added policy in force

### P1. Namedness over capitalization

Label only a name, title, or contextually specific referent. Capitalization alone is not evidence of an entity, and lower-case occupational titles can still be JOB mentions.

Example: Research sector is not an organization; entrepreneur is JOB when it names the role.

Why: Prevents capitalized common nouns from becoming false positives while preserving contextual roles.

### P2. Aliases, abbreviations, and honorifics

Label a conventional alias, initials, or acronym when it independently identifies the same entity. Exclude honorifics and courtesy titles from PERSON unless they are inseparable from the established name.

Example: <PERSON>Manny Pacquiao</PERSON> and <PERSON>Pac-Man</PERSON>; Dr. stays outside Sarah Chen.

Why: Makes short-form mentions reusable without absorbing grammatical titles into names.

### P3. Numeric core and range boundaries

For AMOUNT, include the numeric core, an attached sign or currency symbol, an attached magnitude suffix, and a currency or measurement unit when that unit is integral to the scalar value. Include a hyphenated numeric range as one span. Exclude approximation words and counted category nouns. A word such as meters is inside for physical length but outside when it means a count of utility devices.

Example: about <AMOUNT>$300m</AMOUNT>; <AMOUNT>3 feet 1 inches</AMOUNT>; <AMOUNT>20,000</AMOUNT> meters were installed; <AMOUNT>50</AMOUNT> tickets.

Why: Retains value-changing units while keeping approximation and counted categories out.

### P4. Time expressions and vague temporal language

TIMEDATE includes explicit dates, clock times, anchored relative dates, ages, recurrences, and durations with their time unit. A continuous interval may include its internal range connector when the phrase denotes one interval. Do not label vague words such as moment, eventually, recent, or long term unless the phrase provides a concrete duration, age, recurrence, or calendar/clock anchor.

Example: <TIMEDATE>May 2</TIMEDATE>, <TIMEDATE>18 months</TIMEDATE>, and <TIMEDATE>36-year-old</TIMEDATE>, but moment is left unlabelled.

Why: Separates timeline-bearing expressions from general temporal discourse.

### P5. Job-title extent

A JOB span contains the complete role noun phrase, including seniority or specialization when these change the role, but excludes determiners, employer names, person names, and incidental adjectives.

Example: an <JOB>International Solutions Analyst</JOB> at Harber Inc.

Why: Avoids both bare-head under-labeling and sentence-fragment over-labeling.

### P6. Works, reports, and candidate names

Label the full presented title of a creative or published work, including a titled report or article. A title and subtitle joined by a colon form one span, including the colon. Label a proposed name when the text explicitly presents it as the name of a work or commercial product. Generic references such as the report, the song, or the product remain outside.

Example: <WORKOFART>Project Performance Audit Report</WORKOFART> and proposed <PRODUCT>PawTrak</PRODUCT>.

Why: Handles titles and naming lists consistently without promoting generic document/product nouns.

### P7. Institution versus physical place

Use ORGANIZATION when a named institution or facility acts, employs, publishes, treats, or otherwise behaves as an institution. Use LOCATION when the same name denotes the physical site or destination.

Example: She works at <ORGANIZATION>Cambridge University</ORGANIZATION>; the event is in <LOCATION>Cambridge</LOCATION>.

Why: Makes the baseline context rule operational for schools, hospitals, banks, and venues.

### P8. No nested or overlapping spans

Do not emit nested or overlapping entities. Select the longest contextually correct entity; annotate a short form separately only when it appears as a separate surface mention.

Example: Use one <ORGANIZATION>New York University</ORGANIZATION> span, not LOCATION New York inside it.

Why: The flat token-classification target cannot represent nested spans without contradictory labels.

### P9. Demonyms and adjectival place references

Do not label nationalities, demonyms, or adjectival place modifiers as LOCATION unless the words denote an actual place in context.

Example: American researchers is unlabelled; researchers in <LOCATION>America</LOCATION> is labelled.

Why: Prevents attributes of people or products from being mistaken for locations.

### P10. Every surface mention is evaluated

Evaluate each textual occurrence independently. Repeated named mentions are all labelled, while a later pronoun or generic description is not copied into the entity set.

Example: Google ... Google produces two ORGANIZATION spans; the company is not a third span.

Why: Prevents both missing repeated mentions and coreference-driven false spans.

### P11. Named software and technical artifacts

Treat named software, games, programming languages, platforms, and technical systems as PRODUCT even when they are open source. Generic technology categories, file types, and programming terms remain outside.

Example: <PRODUCT>Minecraft</PRODUCT> and <PRODUCT>Java</PRODUCT>, but plugin and programming language stay outside.

Why: The label inventory has no software class; PRODUCT is the closest stable target for named technical artifacts.

### P12. Handles and anonymized person identifiers

A username, stage name, explicit nickname, or anonymized placeholder is PERSON when it identifies a particular person in context. Generic family terms, occupations, and descriptive epithets are not PERSON.

Example: <PERSON>Person1</PERSON> and <PERSON>Pac-Man</PERSON>, but mum and the guitarist are outside.

Why: Preserves person identity in conversational and privacy-oriented text without labeling generic descriptions.

## Limitations

Fine-grained Few-NERD labels were collapsed into this eight-label ontology; unmappable event/other classes were excluded. Heuristic additions favor precision. The holdout is separated by source split and normalized text, not document lineage.