Datasets:
entity
stringclasses 1
value | token
stringlengths 1
103
⌀ | label
stringclasses 4
values | annotation_id
stringlengths 10
13
⌀ | sentence
int64 0
264
| __index_level_0__
int64 0
8.03k
|
|---|---|---|---|---|---|
Q560307 | Bessie | ENTITY | annot_id_0 | 0 | 0 |
Q560307 | Amelia | ENTITY | annot_id_0 | 0 | 1 |
Q560307 | Emery | ENTITY | annot_id_0 | 0 | 2 |
Q560307 | Head | ENTITY | annot_id_0 | 0 | 3 |
Q560307 | ( | null | null | 0 | 4 |
Q560307 | 6 | null | null | 0 | 5 |
Q560307 | July | null | null | 0 | 6 |
Q560307 | 1937 | null | null | 0 | 7 |
Q560307 | – | null | null | 0 | 8 |
Q560307 | 17 | null | null | 0 | 9 |
Q560307 | April | null | null | 0 | 10 |
Q560307 | 1986 | null | null | 0 | 11 |
Q560307 | ) | null | null | 0 | 12 |
Q560307 | , | null | null | 0 | 13 |
Q560307 | was | EVENT-GR | annot_id_1 | 0 | 14 |
Q560307 | a | null | null | 0 | 15 |
Q560307 | South | null | null | 0 | 16 |
Q560307 | African | null | null | 0 | 17 |
Q560307 | writer | EVENT | annot_id_2 | 0 | 18 |
Q560307 | who | null | null | 0 | 19 |
Q560307 | , | null | null | 0 | 20 |
Q560307 | though | null | null | 0 | 21 |
Q560307 | born | EVENT | annot_id_3 | 0 | 22 |
Q560307 | in | null | null | 0 | 23 |
Q560307 | South | null | null | 0 | 24 |
Q560307 | Africa | null | null | 0 | 25 |
Q560307 | , | null | null | 0 | 26 |
Q560307 | is | null | null | 0 | 27 |
Q560307 | usually | null | null | 0 | 28 |
Q560307 | considered | EVENT | annot_id_4 | 0 | 29 |
Q560307 | Botswana | null | null | 0 | 30 |
Q560307 | 's | null | null | 0 | 31 |
Q560307 | most | null | null | 0 | 32 |
Q560307 | influential | null | null | 0 | 33 |
Q560307 | writer | EVENT | annot_id_5 | 0 | 34 |
Q560307 | . | null | null | 0 | 35 |
Q560307 | She | ENTITY | annot_id_6 | 1 | 36 |
Q560307 | wrote | EVENT | annot_id_7 | 1 | 37 |
Q560307 | novels | null | null | 1 | 38 |
Q560307 | , | null | null | 1 | 39 |
Q560307 | short | null | null | 1 | 40 |
Q560307 | fiction | null | null | 1 | 41 |
Q560307 | and | null | null | 1 | 42 |
Q560307 | autobiographical | null | null | 1 | 43 |
Q560307 | works | null | null | 1 | 44 |
Q560307 | that | null | null | 1 | 45 |
Q560307 | are | null | null | 1 | 46 |
Q560307 | infused | null | null | 1 | 47 |
Q560307 | with | null | null | 1 | 48 |
Q560307 | spiritual | null | null | 1 | 49 |
Q560307 | questioning | null | null | 1 | 50 |
Q560307 | and | null | null | 1 | 51 |
Q560307 | reflection | null | null | 1 | 52 |
Q560307 | . | null | null | 1 | 53 |
Q560307 | Bessie | ENTITY | annot_id_8 | 2 | 54 |
Q560307 | Amelia | ENTITY | annot_id_8 | 2 | 55 |
Q560307 | Emery | ENTITY | annot_id_8 | 2 | 56 |
Q560307 | was | null | null | 2 | 57 |
Q560307 | born | EVENT | annot_id_9 | 2 | 58 |
Q560307 | in | null | null | 2 | 59 |
Q560307 | Pietermaritzburg | null | null | 2 | 60 |
Q560307 | , | null | null | 2 | 61 |
Q560307 | South | null | null | 2 | 62 |
Q560307 | Africa | null | null | 2 | 63 |
Q560307 | , | null | null | 2 | 64 |
Q560307 | the | null | null | 2 | 65 |
Q560307 | child | null | null | 2 | 66 |
Q560307 | of | null | null | 2 | 67 |
Q560307 | a | null | null | 2 | 68 |
Q560307 | " | null | null | 2 | 69 |
Q560307 | white | null | null | 2 | 70 |
Q560307 | " | null | null | 2 | 71 |
Q560307 | woman | null | null | 2 | 72 |
Q560307 | and | null | null | 2 | 73 |
Q560307 | a | null | null | 2 | 74 |
Q560307 | " | null | null | 2 | 75 |
Q560307 | non | null | null | 2 | 76 |
Q560307 | - | null | null | 2 | 77 |
Q560307 | white | null | null | 2 | 78 |
Q560307 | " | null | null | 2 | 79 |
Q560307 | man | null | null | 2 | 80 |
Q560307 | at | null | null | 2 | 81 |
Q560307 | a | null | null | 2 | 82 |
Q560307 | time | null | null | 2 | 83 |
Q560307 | when | null | null | 2 | 84 |
Q560307 | interracial | null | null | 2 | 85 |
Q560307 | relationships | null | null | 2 | 86 |
Q560307 | were | null | null | 2 | 87 |
Q560307 | illegal | null | null | 2 | 88 |
Q560307 | in | null | null | 2 | 89 |
Q560307 | South | null | null | 2 | 90 |
Q560307 | Africa | null | null | 2 | 91 |
Q560307 | . | null | null | 2 | 92 |
Q560307 | Bessie | null | null | 3 | 93 |
Q560307 | 's | null | null | 3 | 94 |
Q560307 | mother | null | null | 3 | 95 |
Q560307 | , | null | null | 3 | 96 |
Q560307 | Bessie | null | null | 3 | 97 |
Q560307 | Amelia | null | null | 3 | 98 |
Q560307 | Emery | null | null | 3 | 99 |
WikiBio @ ACL 2023
This is the repository of the WikiBio corpus, which is described in the following paper:
Wikibio: a Semantic Resource for the Intersectional Analysis of Biographical Events
Please use this reference to cite our work
@inproceedings{stranisci-etal-2023-wikibio,
title = "{W}iki{B}io: a Semantic Resource for the Intersectional Analysis of Biographical Events",
author = "Stranisci, Marco Antonio and Damiano, Rossana and Mensa, Enrico and Patti, Viviana and Radicioni, Daniele and Caselli, Tommaso",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.691",
pages = "12370--12384",
abstract = "Biographical event detection is a relevant task that allows for the exploration and comparison of the ways in which people{'}s lives are told and represented. This may support several real-life applications in digital humanities and in works aimed at exploring bias about minoritized groups. Despite that, there are no corpora and models specifically designed for this task. In this paper we fill this gap by presenting a new corpus annotated for biographical event detection. The corpus, which includes 20 Wikipedia biographies, was aligned with 5 existing corpora in order to train a model for the biographical event detection task. The model was able to detect all mentions of the target-entity in a biography with an F-score of 0.808 and the entity-related events with an F-score of 0.859. Finally, the model was used for performing an analysis of biases about women and non-Western people in Wikipedia biographies.",
}
Structure of the dataset
Each row in the corpus has information about the target entity of the biography (entity), the annotated token, the label, the label id, and the sentence number
Labels are the following:
- ENTITY (a mention of the entity)
- EVENT (all generic event like win, study)
- EVENT-GR (semantically void predicates like be and have. Take a look at the work of Bonial and Palmer on light verbs for more information)
- EVENT-ASP (aspectual events like start, finish)
- EVENT-MOD (events or modifier that modify the actual occurrence of the event. E.g., not, try)
Data Statement for WikiBio corpus
Data set name: WikiBio
A. CURATION RATIONALE
WikiBio is a Corpus of 20 writers' biographies annotated for the biographical event detection task.
- Data Collection Process. All the annotated documents were gathered from the English Wikipedia. In order to retain only relevant document we relied on Wikidata for the collection of all the entities having as occupation "writer", "novelist", or "poet", born in an African country or being African-American. We then selected only ones belonging to the Silent Generation (born between 1928 and 1945). From that collection we sampled 10 African writers and 10 African American Writers whose biographies' length were higher than 200.000 tokens.
- Time Period. All documents have been gathered from English Wikipedia in Winter 2021.
B. LANGUAGE VARIETY
- BCP-47 language tag: en
- Language variety description: English
C. SPEAKER DEMOGRAPHIC
N/A
D. ANNOTATOR DEMOGRAPHIC
Annotator #1: Age: 38; Gender: male; Race/ethnicity: caucasian; Native language: Italian; Socioeconomic status:n/a Training in linguistics/other relevant discipline: PhD student in Computer Science.
Annotator #2: Age: 50; Gender: female; Race/ethnicity: caucasian; Native language: Italian; Socioeconomic status:n/a Training in linguistics/other relevant discipline: Associate Professor in Computer Science
Annotator #3: Age: 30; Gender: male; Race/ethnicity: caucasian; Native language: Italian; Socioeconomic status:n/a Training in linguistics/other relevant discipline: Researcher in Computer Science
All annotators are near-native speakers of British English, having a long experience in annotating data for the specific task (event and entity detection).
E. SPEECH SITUATION
N/A
F. TEXT CHARACTERISTICS
Wikipedia documents
G. ANNOTATORS' COMPENSATION
Annotators' activity is part of their effort related to the development of the present work, which was economically recognized within their contracts with the Academic Institution they are working for.
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