Datasets:
File size: 4,625 Bytes
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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](https://arxiv.org/abs/1906.07348); via
[`naist-nlp/zeshel`](https://huggingface.co/datasets/naist-nlp/zeshel)).
Reformatted into the schema used across the
[Chunk-level Retrieval Eval](https://huggingface.co/collections/bowang0911/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
```bibtex
@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"
}
```
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