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"Un": false,
"SA": false
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"id_2-start": 17,
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"relation-type": "topic",
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"Un": false,
"SA": false
},
{
"id_1-start": 25,
"id_1-end": 26,
"id_2-start": 24,
"id_2-end": 24,
"relation-type": "general-affiliation",
"Exp": "nationality",
"Un": false,
"SA": false
}
] |
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"entity-type": "researcher"
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"entity-type": "researcher"
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{
"id-start": 11,
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"entity-type": "researcher"
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{
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"entity-type": "algorithm"
},
{
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"entity-type": "algorithm"
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{
"id-start": 24,
"id-end": 24,
"entity-type": "algorithm"
}
] | [
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"relation-type": "origin",
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"SA": false
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"id_2-start": 24,
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"relation-type": "part-of",
"Exp": "",
"Un": false,
"SA": false
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"id_2-start": 15,
"id_2-end": 16,
"relation-type": "named",
"Exp": "",
"Un": false,
"SA": false
}
] |
ai-train-97 | [
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"the",
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"sinc",
"function",
"is",
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"defined",
"for",
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"entity-type": "field"
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{
"id-start": 5,
"id-end": 6,
"entity-type": "field"
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{
"id-start": 9,
"id-end": 11,
"entity-type": "algorithm"
}
] | [
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"SA": false
},
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"id_2-start": 5,
"id_2-end": 6,
"relation-type": "part-of",
"Exp": "",
"Un": false,
"SA": false
}
] |
ai-train-98 | [
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"Linguistics",
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"the",
"International",
"Committee",
"on",
"Computational",
"Linguistics",
"(",
"ICCL",
")",
"."
] | [
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"entity-type": "field"
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{
"id-start": 9,
"id-end": 10,
"entity-type": "researcher"
},
{
"id-start": 18,
"id-end": 21,
"entity-type": "conference"
},
{
"id-start": 24,
"id-end": 28,
"entity-type": "organisation"
},
{
"id-start": 30,
"id-end": 30,
"entity-type": "organisation"
}
] | [
{
"id_1-start": 2,
"id_1-end": 3,
"id_2-start": 9,
"id_2-end": 10,
"relation-type": "origin",
"Exp": "coined_term",
"Un": false,
"SA": false
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{
"id_1-start": 9,
"id_1-end": 10,
"id_2-start": 18,
"id_2-end": 21,
"relation-type": "role",
"Exp": "",
"Un": false,
"SA": false
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"relation-type": "role",
"Exp": "",
"Un": false,
"SA": false
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{
"id_1-start": 30,
"id_1-end": 30,
"id_2-start": 24,
"id_2-end": 28,
"relation-type": "named",
"Exp": "",
"Un": false,
"SA": false
}
] |
ai-train-99 | [
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",",
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"2547-2553",
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"2011",
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"DPD",
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"the",
"digital",
"pre-distorter",
"polynomials",
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"and",
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"MSE",
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",",
"the",
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"of",
"the",
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"."
] | [
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"id-start": 8,
"id-end": 11,
"entity-type": "misc"
},
{
"id-start": 16,
"id-end": 16,
"entity-type": "misc"
},
{
"id-start": 31,
"id-end": 33,
"entity-type": "metrics"
},
{
"id-start": 35,
"id-end": 35,
"entity-type": "metrics"
}
] | [
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"id_1-start": 35,
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"id_2-start": 31,
"id_2-end": 33,
"relation-type": "named",
"Exp": "",
"Un": false,
"SA": false
}
] |
ai-train-100 | [
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",",
"(",
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",",
"1947",
",",
"Chișinău",
",",
"Moldavian",
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",",
"Soviet",
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",",
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{
"id-start": 10,
"id-end": 10,
"entity-type": "location"
},
{
"id-start": 12,
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"entity-type": "location"
},
{
"id-start": 15,
"id-end": 16,
"entity-type": "country"
},
{
"id-start": 20,
"id-end": 20,
"entity-type": "location"
},
{
"id-start": 22,
"id-end": 22,
"entity-type": "country"
},
{
"id-start": 37,
"id-end": 43,
"entity-type": "organisation"
},
{
"id-start": 46,
"id-end": 49,
"entity-type": "organisation"
},
{
"id-start": 51,
"id-end": 51,
"entity-type": "location"
},
{
"id-start": 58,
"id-end": 59,
"entity-type": "organisation"
}
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"id_1-start": 0,
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"relation-type": "physical",
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{
"id_1-start": 0,
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"id_2-start": 58,
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"relation-type": "role",
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{
"id_1-start": 12,
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"id_2-start": 15,
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},
{
"id_1-start": 37,
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"id_2-start": 46,
"id_2-end": 49,
"relation-type": "part-of",
"Exp": "",
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},
{
"id_1-start": 46,
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"id_2-start": 51,
"id_2-end": 51,
"relation-type": "physical",
"Exp": "",
"Un": false,
"SA": false
},
{
"id_1-start": 58,
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"relation-type": "part-of",
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}
] |
Dataset Card for CrossRE
Dataset Summary
CrossRE is a new, freely-available crossdomain benchmark for RE, which comprises six distinct text domains and includes multilabel annotations. It includes the following domains: news, politics, natural science, music, literature and artificial intelligence. The semantic relations are annotated on top of CrossNER (Liu et al., 2021), a cross-domain dataset for NER which contains domain-specific entity types. The dataset contains 17 relation labels for the six domains: PART-OF, PHYSICAL, USAGE, ROLE, SOCIAL, GENERAL-AFFILIATION, COMPARE, TEMPORAL, ARTIFACT, ORIGIN, TOPIC, OPPOSITE, CAUSE-EFFECT, WIN-DEFEAT, TYPEOF, NAMED, and RELATED-TO.
For details, see the paper: https://arxiv.org/abs/2210.09345
Supported Tasks and Leaderboards
Languages
The language data in CrossRE is in English (BCP-47 en)
Dataset Structure
Data Instances
news
- Size of downloaded dataset files: 0.24 MB
- Size of the generated dataset: 0.22 MB
An example of 'train' looks as follows:
{
"doc_key": "news-train-1",
"sentence": ["EU", "rejects", "German", "call", "to", "boycott", "British", "lamb", "."],
"ner": [
{"id-start": 0, "id-end": 0, "entity-type": "organisation"},
{"id-start": 2, "id-end": 3, "entity-type": "misc"},
{"id-start": 6, "id-end": 7, "entity-type": "misc"}
],
"relations": [
{"id_1-start": 0, "id_1-end": 0, "id_2-start": 2, "id_2-end": 3, "relation-type": "opposite", "Exp": "rejects", "Un": False, "SA": False},
{"id_1-start": 2, "id_1-end": 3, "id_2-start": 6, "id_2-end": 7, "relation-type": "opposite", "Exp": "calls_for_boycot_of", "Un": False, "SA": False},
{"id_1-start": 2, "id_1-end": 3, "id_2-start": 6, "id_2-end": 7, "relation-type": "topic", "Exp": "", "Un": False, "SA": False}
]
}
politics
- Size of downloaded dataset files: 0.73 MB
- Size of the generated dataset: 0.65 MB
An example of 'train' looks as follows:
{
"doc_key": "politics-train-1",
"sentence": ["Parties", "with", "mainly", "Eurosceptic", "views", "are", "the", "ruling", "United", "Russia", ",", "and", "opposition", "parties", "the", "Communist", "Party", "of", "the", "Russian", "Federation", "and", "Liberal", "Democratic", "Party", "of", "Russia", "."],
"ner": [
{"id-start": 8, "id-end": 9, "entity-type": "politicalparty"},
{"id-start": 15, "id-end": 20, "entity-type": "politicalparty"},
{"id-start": 22, "id-end": 26, "entity-type": "politicalparty"}
],
"relations": [
{"id_1-start": 8, "id_1-end": 9, "id_2-start": 15, "id_2-end": 20, "relation-type": "opposite", "Exp": "in_opposition", "Un": False, "SA": False},
{"id_1-start": 8, "id_1-end": 9, "id_2-start": 22, "id_2-end": 26, "relation-type": "opposite", "Exp": "in_opposition", "Un": False, "SA": False}
]
}
science
- Size of downloaded dataset files: 0.59 MB
- Size of the generated dataset: 0.54 MB
An example of 'train' looks as follows:
{
"doc_key": "science-train-1",
"sentence": ["They", "may", "also", "use", "Adenosine", "triphosphate", ",", "Nitric", "oxide", ",", "and", "ROS", "for", "signaling", "in", "the", "same", "ways", "that", "animals", "do", "."],
"ner": [
{"id-start": 4, "id-end": 5, "entity-type": "chemicalcompound"},
{"id-start": 7, "id-end": 8, "entity-type": "chemicalcompound"},
{"id-start": 11, "id-end": 11, "entity-type": "chemicalcompound"}
],
"relations": []
}
music
- Size of downloaded dataset files: 0.73 MB
- Size of the generated dataset: 0.64 MB
An example of 'train' looks as follows:
{
"doc_key": "music-train-1",
"sentence": ["In", "2003", ",", "the", "Stade", "de", "France", "was", "the", "primary", "site", "of", "the", "2003", "World", "Championships", "in", "Athletics", "."],
"ner": [
{"id-start": 4, "id-end": 6, "entity-type": "location"},
{"id-start": 13, "id-end": 17, "entity-type": "event"}
],
"relations": [
{"id_1-start": 13, "id_1-end": 17, "id_2-start": 4, "id_2-end": 6, "relation-type": "physical", "Exp": "", "Un": False, "SA": False}
]
}
literature
- Size of downloaded dataset files: 0.64 MB
- Size of the generated dataset: 0.57 MB
An example of 'train' looks as follows:
{
"doc_key": "literature-train-1",
"sentence": ["In", "1351", ",", "during", "the", "reign", "of", "Emperor", "Toghon", "Temür", "of", "the", "Yuan", "dynasty", ",", "93rd-generation", "descendant", "Kong", "Huan", "(", "孔浣", ")", "'", "s", "2nd", "son", "Kong", "Shao", "(", "孔昭", ")", "moved", "from", "China", "to", "Korea", "during", "the", "Goryeo", ",", "and", "was", "received", "courteously", "by", "Princess", "Noguk", "(", "the", "Mongolian-born", "wife", "of", "the", "future", "king", "Gongmin", ")", "."],
"ner": [
{"id-start": 7, "id-end": 9, "entity-type": "person"},
{"id-start": 12, "id-end": 13, "entity-type": "country"},
{"id-start": 17, "id-end": 18, "entity-type": "writer"},
{"id-start": 20, "id-end": 20, "entity-type": "writer"},
{"id-start": 26, "id-end": 27, "entity-type": "writer"},
{"id-start": 29, "id-end": 29, "entity-type": "writer"},
{"id-start": 33, "id-end": 33, "entity-type": "country"},
{"id-start": 35, "id-end": 35, "entity-type": "country"},
{"id-start": 38, "id-end": 38, "entity-type": "misc"},
{"id-start": 45, "id-end": 46, "entity-type": "person"},
{"id-start": 49, "id-end": 50, "entity-type": "misc"},
{"id-start": 55, "id-end": 55, "entity-type": "person"}
],
"relations": [
{"id_1-start": 7, "id_1-end": 9, "id_2-start": 12, "id_2-end": 13, "relation-type": "role", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 7, "id_1-end": 9, "id_2-start": 12, "id_2-end": 13, "relation-type": "temporal", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 17, "id_1-end": 18, "id_2-start": 26, "id_2-end": 27, "relation-type": "social", "Exp": "family", "Un": False, "SA": False},
{"id_1-start": 20, "id_1-end": 20, "id_2-start": 17, "id_2-end": 18, "relation-type": "named", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 26, "id_1-end": 27, "id_2-start": 33, "id_2-end": 33, "relation-type": "physical", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 26, "id_1-end": 27, "id_2-start": 35, "id_2-end": 35, "relation-type": "physical", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 26, "id_1-end": 27, "id_2-start": 38, "id_2-end": 38, "relation-type": "temporal", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 26, "id_1-end": 27, "id_2-start": 45, "id_2-end": 46, "relation-type": "social", "Exp": "greeted_by", "Un": False, "SA": False},
{"id_1-start": 29, "id_1-end": 29, "id_2-start": 26, "id_2-end": 27, "relation-type": "named", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 45, "id_1-end": 46, "id_2-start": 55, "id_2-end": 55, "relation-type": "social", "Exp": "marriage", "Un": False, "SA": False},
{"id_1-start": 49, "id_1-end": 50, "id_2-start": 45, "id_2-end": 46, "relation-type": "named", "Exp": "", "Un": False, "SA": False}
]
}
ai
- Size of downloaded dataset files: 0.51 MB
- Size of the generated dataset: 0.46 MB
An example of 'train' looks as follows:
{
"doc_key": "ai-train-1",
"sentence": ["Popular", "approaches", "of", "opinion-based", "recommender", "system", "utilize", "various", "techniques", "including", "text", "mining", ",", "information", "retrieval", ",", "sentiment", "analysis", "(", "see", "also", "Multimodal", "sentiment", "analysis", ")", "and", "deep", "learning", "X.Y.", "Feng", ",", "H.", "Zhang", ",", "Y.J.", "Ren", ",", "P.H.", "Shang", ",", "Y.", "Zhu", ",", "Y.C.", "Liang", ",", "R.C.", "Guan", ",", "D.", "Xu", ",", "(", "2019", ")", ",", ",", "21", "(", "5", ")", ":", "e12957", "."],
"ner": [
{"id-start": 3, "id-end": 5, "entity-type": "product"},
{"id-start": 10, "id-end": 11, "entity-type": "field"},
{"id-start": 13, "id-end": 14, "entity-type": "task"},
{"id-start": 16, "id-end": 17, "entity-type": "task"},
{"id-start": 21, "id-end": 23, "entity-type": "task"},
{"id-start": 26, "id-end": 27, "entity-type": "field"},
{"id-start": 28, "id-end": 29, "entity-type": "researcher"},
{"id-start": 31, "id-end": 32, "entity-type": "researcher"},
{"id-start": 34, "id-end": 35, "entity-type": "researcher"},
{"id-start": 37, "id-end": 38, "entity-type": "researcher"},
{"id-start": 40, "id-end": 41, "entity-type": "researcher"},
{"id-start": 43, "id-end": 44, "entity-type": "researcher"},
{"id-start": 46, "id-end": 47, "entity-type": "researcher"},
{"id-start": 49, "id-end": 50, "entity-type": "researcher"}
],
"relations": [
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 10, "id_2-end": 11, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 10, "id_2-end": 11, "relation-type": "usage", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 13, "id_2-end": 14, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 13, "id_2-end": 14, "relation-type": "usage", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 16, "id_2-end": 17, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 16, "id_2-end": 17, "relation-type": "usage", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 26, "id_2-end": 27, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 26, "id_2-end": 27, "relation-type": "usage", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 21, "id_1-end": 23, "id_2-start": 16, "id_2-end": 17, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 21, "id_1-end": 23, "id_2-start": 16, "id_2-end": 17, "relation-type": "type-of", "Exp": "", "Un": False, "SA": False}
]
}
Data Fields
The data fields are the same among all splits.
doc_key
: the instance id of this sentence, astring
feature.sentence
: the list of tokens of this sentence, obtained with spaCy, alist
ofstring
features.ner
: the list of named entities in this sentence, alist
ofdict
features.id-start
: the start index of the entity, aint
feature.id-end
: the end index of the entity, aint
feature.entity-type
: the type of the entity, astring
feature.
relations
: the list of relations in this sentence, alist
ofdict
features.id_1-start
: the start index of the first entity, aint
feature.id_1-end
: the end index of the first entity, aint
feature.id_2-start
: the start index of the second entity, aint
feature.id_2-end
: the end index of the second entity, aint
feature.relation-type
: the type of the relation, astring
feature.Exp
: the explanation of the relation type assigned, astring
feature.Un
: uncertainty of the annotator, abool
feature.SA
: existence of syntax ambiguity which poses a challenge for the annotator, abool
feature.
Data Splits
Sentences
Train | Dev | Test | Total | |
---|---|---|---|---|
news | 164 | 350 | 400 | 914 |
politics | 101 | 350 | 400 | 851 |
science | 103 | 351 | 400 | 854 |
music | 100 | 350 | 399 | 849 |
literature | 100 | 400 | 416 | 916 |
ai | 100 | 350 | 431 | 881 |
------------ | ------- | ------- | ------- | ------- |
total | 668 | 2,151 | 2,46 | 5,265 |
Relations
Train | Dev | Test | Total | |
---|---|---|---|---|
news | 175 | 300 | 396 | 871 |
politics | 502 | 1,616 | 1,831 | 3,949 |
science | 355 | 1,340 | 1,393 | 3,088 |
music | 496 | 1,861 | 2,333 | 4,690 |
literature | 397 | 1,539 | 1,591 | 3,527 |
ai | 350 | 1,006 | 1,127 | 2,483 |
------------ | ------- | ------- | ------- | ------- |
total | 2,275 | 7,662 | 8,671 | 18,608 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@inproceedings{bassignana-plank-2022-crossre,
title = "Cross{RE}: A {C}ross-{D}omain {D}ataset for {R}elation {E}xtraction",
author = "Bassignana, Elisa and Plank, Barbara",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
year = "2022",
publisher = "Association for Computational Linguistics"
}
Contributions
Thanks to @phucdev for adding this dataset.
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