ANNOTATION_GUIDELINES.md

Annotation Guidelines

Version 1.0. Defines what counts as an entity span in this dataset. This is the specification the extraction was held to, the rubric the human review judged against, and the answer to "what scheme is this?"

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1. Scope

This dataset annotates surface spans of named entities in reference transcripts of English speech, for measuring whether an ASR system transcribes those entities correctly.

It approximates OntoNotes 5.0 name conventions, with the deviations in §5. It is not a full OntoNotes annotation: only the types in §2 are covered, spans are flat, and no coreference is recorded.

This is not a text-NER training corpus. Spans are targets for transcription accuracy. Where the two purposes conflict, transcription accuracy wins — this is why titles are excluded (§4.3) and why the minimum-span rule (§4.1) applies.

2. Entity types

type	covers	spans
PERSON	named individuals	1,193
GPE	countries, states, cities, towns — polities	857
ORG	companies, agencies, institutions, teams, committees	511
NORP	nationality, religious, ethnic and political groups, including adjectives	246
LOC	physical places that are not polities: regions, landmarks, water, celestial bodies	223
OTHER_NAMED	named events, works, products, laws and facilities	215

Distinctions that decide edge cases:

• A company named after a place is ORG, not GPE. Northern Rock is a bank.
• A street is LOC, not GPE.
• An ethnic or national group is NORP, not PERSON.
• A stadium or building is OTHER_NAMED, not LOC.

Where these came from

The source delivery annotates three levels: Noun Type, Entity Type, Entity subtype. This release carries a mapping of the middle level:

delivery label	released as	method
Names	PERSON	deterministic rename
State based location	GPE	deterministic rename
Geographic location	LOC	deterministic rename
Institutions	ORG	deterministic rename
Ethnicity/nationality demonym	NORP	retyped, see below
Concepts, Objects, Living things, Titles	classified individually	see below

2,813 spans were renamed by rule with no model involvement.

NORP. The delivery files demonyms under Noun Type Person with Entity subtype Ethnicity/nationality demonym. American is not a person. These were retyped rather than deleted, since the annotation itself is intentional upstream; only the label was wrong.

OTHER_NAMED and the removals. 522 spans carried delivery labels that are not entity types — a class holding real entities (A World's Fair, Operation Dragoon) alongside things that are not names at all (planned lots phase three, coffee shop or diner). Each was classified by two independent model passes over a closed inventory. Raw agreement 82.5%, Cohen's kappa 0.785. After collapsing the fine-grained classes the two passes could not reliably separate — EVENT, WORK_OF_ART, PRODUCT, LAW, FAC — into OTHER_NAMED, 351 were auto-decided and 69 were adjudicated by hand. 90 spans were judged not to be entities and were removed from the release.

3. The exactness contract

Every span is an exact, contiguous substring of the reference transcript, character for character. Verified on every build; the release process fails if it is ever violated.

• Case follows the reference. Belebele/FLEURS references are cased; MLCommons references are verbatim lowercase. A PERSON span in MLCommons is sami baghdady, not Sami Baghdady. Lowercase is correct, not a defect.
• No normalization, expansion or correction is applied to spans. A misspelling in the reference appears in the span.
• Numerals appear as the reference has them, spelled out or not.
• 98.8% of spans additionally carry character offsets (start, end). The remainder are cases where one string was annotated more times than it occurs.

The canonical scorer normalizes case and punctuation, so a system that capitalizes correctly is not penalized on the lowercase subset.

4. Boundary rules

4.1 Minimum span

The shortest contiguous string that names the entity. No context, no disambiguation, no trailing generic nouns. adsb system -> adsb.

4.2 Possessives

The possessive clitic is excluded: Vatican City's -> Vatican City.

An internal possessive that is part of the name is kept: Addenbrooke's Hospital, St. Peter's Square, Children's Hospital of Pittsburgh are each one span. Splitting on the apostrophe is wrong for the common case and was reverted during preparation.

4.3 Titles and roles

Excluded from PERSON spans, before or after the name. vice chair sami baghdady -> sami baghdady. selectman jim williams -> jim williams. Rationale: the title is a common noun any recognizer gets right, and including it dilutes the measurement. Exception: a title inside an organizational name stays (Office of the Town Administrator).

4.4 Nationality, religious and political adjectives

Annotated as NORP, not folded into an adjacent span. Vichy French -> Vichy as LOC; the adjective is separate. the German company Siemens -> Siemens as ORG. Compound demonyms are single units: South African is one NORP span, never truncated to South.

4.5 Adjacent entities

Two entities side by side are two spans. Boston Massachusetts is two GPE spans. Apposition and juxtaposition both fall under this.

4.6 Determiners

A leading the is excluded unless part of the official name. the Pentagon -> Pentagon. The Hague keeps it.

4.7 Organization suffixes

Legal and corporate suffixes that are part of the name are included (Acme Corp, Northeastern University). Trailing descriptors are not (the Acme Corp facility -> Acme Corp).

4.8 Disfluencies

Spontaneous speech contains restarts. Annotate the completed mention only: sami— sami baghdady -> sami baghdady.

4.9 Repeated mentions

Every mention is annotated and scored independently.

4.10 Nesting

Spans are flat and non-overlapping. Where a name contains another (Bank of Boston), only the outer entity is annotated.

5. Deviations from OntoNotes 5.0

1. Reduced inventory. No DATE, MONEY, CARDINAL, PERCENT, TIME, QUANTITY.
2. EVENT, WORK_OF_ART, PRODUCT, LAW and FAC are merged into OTHER_NAMED, because two independent annotation passes could not separate them reliably. Publishing distinctions that cannot be reproduced would be misleading.
3. Flat spans; OntoNotes permits nesting.
4. Case follows the source transcript, including all-lowercase references.

6. Difficulty tiers

tier	definition	example
A	Discriminative. Multi-token names, rare surnames, uncommon place and organization names.	jong seork park, wellesley town hall
B	Trivial. Bare common given names, very high-frequency place names, and all demonyms.	david, jim, Europe, american

Report tier A only for the headline metric. Tier B spans are transcribed correctly by essentially any English recognizer and do not discriminate between systems. They are retained so users can re-tier or report both; a tier A+B figure is inflated and not comparable.

2,334 spans are tier A and 911 are tier B (28.1%). Tier B is heavier in the MLCommons subset (33.5%) than in Belebele/FLEURS (22.6%), because meeting speech is full of bare first names.

Tier assignment maps §2 types onto coarse classes (PERSON->person, GPE/LOC->location, ORG->organization, NORP->demonym, OTHER_NAMED->other), then applies an embedded frequency list. The list is in assign_tiers.py so the assignment is reproducible without a network fetch; --audit-types prints the mapping and reports anything unmapped.

7. Annotation provenance

Both the type labels and the surface spans originate from automated processes, not from human annotators writing from scratch.

• Spans were recovered by LLM extraction after a capitalization heuristic returned zero spans on the lowercase MLCommons references.
• Types were derived from the source delivery as described in §2.
• A deterministic boundary sweep then applied §4, re-validating every edit against §3 and reverting anything that broke the contract.
• A stratified sample was reviewed by hand; the result is in the dataset card.

Substring validation confirms a span exists in its reference. It does not confirm the span is correct. The card reports those separately.

8. Review procedure

Each sampled span is judged against this document:

verdict	meaning
correct	right type, right boundaries
boundary	right entity, wrong extent (§4 violation)
type	right extent, wrong type (§2)
spurious	not an entity of any covered type
tier	correct, but tiered wrong (§6)

Sampling is stratified across subset and alignment status. The reported rate is correct / n with a Wilson score interval. Reviewers read the full transcript, not only the span — several defect classes are visible only in context.

9. Known limitations

• Boundary artifacts remain in an unmeasured portion of unreviewed spans.
• Three types (OTHER_NAMED, and the NORP retypings) involved model judgement rather than a rule; the agreement figure is in §2.
• English only.
• Two domains: read encyclopedic prose, and US municipal and legislative meetings. Do not generalize to conversational, telephony or broadcast speech.
• MLCommons references are verbatim transcripts and carry disfluencies and transcription noise inherited from the source corpus.
• 47 clips carry zero annotations after non-entity removal.

10. Changelog

version	change
1.0	initial release