README.md

metadata

language:
  - en
task_categories:
  - text-retrieval
size_categories:
  - 10K<n<100K
dataset_info:
  - config_name: documents
    features:
      - name: chunk_id
        dtype: string
      - name: chunk
        dtype: string
      - name: source_url
        dtype: string
      - name: title
        dtype: string
      - name: chunk_idx
        dtype: int64
      - name: chunk_start_char
        dtype: int64
      - name: chunk_end_char
        dtype: int64
    splits:
      - name: test
        num_bytes: 137244943
        num_examples: 70140
    download_size: 78083409
    dataset_size: 137244943
  - config_name: queries
    features:
      - name: original_query
        dtype: string
      - name: query
        dtype: string
      - name: answer
        list: string
      - name: score
        list: int64
      - name: source_url
        list: string
      - name: frag_start_char
        list: int64
      - name: frag_end_char
        list: int64
      - name: n_gold
        dtype: int64
    splits:
      - name: test
        num_bytes: 211299459
        num_examples: 10000
    download_size: 95617096
    dataset_size: 211299459
configs:
  - config_name: documents
    data_files:
      - split: test
        path: documents/test-*
  - config_name: queries
    data_files:
      - split: test
        path: queries/test-*

ZeShEL — Entity-Linking Chunk-level Retrieval Eval

Contextualized chunk-level (query2chunk) retrieval eval for entity linking, built from the ZeShEL (Zero-Shot Entity Linking) dataset (Logeswaran et al., ACL 2019; via naist-nlp/zeshel).

Reformatted into the schema used across the Chunk-level Retrieval Eval collection.

Task

Entity linking as retrieval: given a mention in context, retrieve the correct entity's description from a corpus of entity descriptions. Unlike the QA benchmarks in this collection, the query is a mention (marked inline with [START_ENT] … [END_ENT]) and the gold "chunk" is the linked entity's description.

Because entity linking's target is an entity (represented by a whole description), each entity description is kept as one chunk — so every mention resolves to exactly one gold chunk. This is the natural EL unit and avoids arbitrary sub-entity chunking.

Scope

Restricted to ZeShEL's held-out test domains (the standard zero-shot eval): star_trek, forgotten_realms, lego, yugioh. Source: Wikia/Fandom.

	count
entity descriptions (corpus)	70,140
query mentions (test)	10,000
test domains	4

Configs

documents (one chunk per entity)

field	type	notes
chunk_id	string	ZeShEL entity id
chunk	string	entity description text
source_url	string	entity id (identity of the entity)
title	string	entity name
chunk_idx	int64	always 0 (one chunk per entity)
chunk_start_char / chunk_end_char	int64	0 .. len(description)

queries

field	type	notes
original_query	string	raw mention context (unmarked)
query	string	context with the mention wrapped in [START_ENT] … [END_ENT]
answer	list[string]	gold entity name(s)
score	list[int64]	1 per gold
source_url	list[string]	gold entity id(s)
frag_start_char / frag_end_char	list[int64]	span of the gold entity description (0 .. len)
n_gold	int64	number of gold entities (almost always 1)

Gold matching

A chunk is gold if it shares source_url (entity id) with a gold fragment and overlaps its [frag_start_char, frag_end_char). Since each entity is one chunk and the gold fragment is the whole description, every mention maps to exactly one gold chunk (verified: 10,000 / 10,000).

Provenance

Built from naist-nlp/zeshel dictionary (entity descriptions → corpus, filtered to the 4 test domains) and data/test (mentions → queries). Records containing multiple mention spans are expanded to one query per span (10,000 total, matching ZeShEL's canonical test set). Mentions are marked using the dataset's char offsets.

Citation

@inproceedings{logeswaran2019zeshel,
    title  = "Zero-Shot Entity Linking by Reading Entity Descriptions",
    author = "Logeswaran, Lajanugen and Chang, Ming-Wei and Lee, Kenton and
              Toutanova, Kristina and Devlin, Jacob and Lee, Honglak",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for
                 Computational Linguistics (ACL)",
    year   = "2019",
    url    = "https://arxiv.org/abs/1906.07348"
}