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] | [
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"Leapfrog Ventures",
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"Peter Rip"
],
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"Peter Rip",
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"Leapfrog Ventures"
]
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"type": "ORGANIZATION"
},
"t": {
"text": "Peter Rip",
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"end": 20,
"type": "PERSON"
},
"type": "/business/company/founders"
},
{
"h": {
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"t": {
"text": "Leapfrog Ventures",
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}
] |
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] | [
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"end": 3,
"type": "LOCATION"
},
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}
] |
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",",
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"."
] | [
[
"George Balanchine",
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"New York City Ballet"
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] | [
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},
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},
{
"h": {
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"t": {
"text": "Manhattan",
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"end": 1,
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},
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}
] |
[
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":",
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"Boulevard",
",",
"Bayside",
",",
"Queens",
";",
"(718)631-7100",
"."
] | [
[
"Queens",
"/location/location/contains",
"Bayside"
],
[
"Bayside",
"/location/neighborhood/neighborhood_of",
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}
] |
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{
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},
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"end": 16,
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},
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[
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},
"t": {
"text": "Astoria",
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"end": 20,
"type": "LOCATION"
},
"type": "/location/location/contains"
},
{
"h": {
"text": "Astoria",
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"end": 20,
"type": "LOCATION"
},
"t": {
"text": "Queens",
"start": 21,
"end": 22,
"type": "LOCATION"
},
"type": "/location/neighborhood/neighborhood_of"
}
] |
[
"INSIDE",
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"NEWS",
"Correction",
":",
"April",
"4",
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",",
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"a",
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"Zoe Cruz",
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"text": "Morgan Stanley",
"start": 23,
"end": 25,
"type": "ORGANIZATION"
},
"type": "/business/person/company"
}
] |
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] | [
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"John J. Mack",
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]
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] | [
{
"h": {
"text": "John J. Mack",
"start": 10,
"end": 13,
"type": "PERSON"
},
"t": {
"text": "Morgan Stanley",
"start": 1,
"end": 3,
"type": "PERSON"
},
"type": "/business/person/company"
}
] |
Dataset Card for NYT-multi
Dataset Summary
The original NYT dataset by Riedel et al. (2010) consists of New York Times news articles from 1987-2007 that was distantly annotated with relations using FreeBase. The original dataset consisted of 1.18M sentences. It is available here: https://iesl.cs.umass.edu/riedel/ecml/
Zeng et al. (2018) that filtered out sentences with more than 100 words and sentences without an active relation, leaving 66195 sentences.
They randomly selected 5000 sentences from it as the test set, 5000 sentences as the validation set and the rest 56195 sentences are used as train set.
The resulting dataset called NYT-multi features overlapping entities across three entity types and 24 relation types.
You can access the raw version from Zeng et al. (2018) using datasets.load_dataset("DFKI-SLT/nyt-multi", config="raw")
.
The original data is available here: https://github.com/xiangrongzeng/copy_re
The data was further pre-processed with the StanfordCoreNLP by Yu et al. (2020): https://github.com/yubowen-ph/JointER
We converted the data into a more readable JSON format and used it for the default version of the dataset.
Languages
The language in the dataset is English.
Dataset Structure
Dataset Instances
default
An example of 'train' looks as follows:
{
"tokens": ["Massachusetts", "ASTON", "MAGNA", "Great", "Barrington", ";", "also", "at", "Bard", "College", ",", "Annandale-on-Hudson", ",", "N.Y.", ",", "July", "1-Aug", "."],
"spo_list": [["Annandale-on-Hudson", "/location/location/contains", "Bard College"]],
"pos_tags": ["NNP", "NNP", "NNP", "NNP", "NNP", ":", "RB", "IN", "NNP", "NNP", ",", "NNP", ",", "NNP", ",", "NNP", "NNP", "."],
"relations": [
{
"h": {"text": "Annandale-on-Hudson", "start": 11, "end": 12, "type": "LOCATION"},
"t": {"text": "Bard College", "start": 8, "end": 10, "type": "ORGANIZATION"},
"type": "/location/location/contains"
}
]
}
raw
An example of 'train' looks as follows:
{
"sentText": "Massachusetts ASTON MAGNA Great Barrington ; also at Bard College , Annandale-on-Hudson , N.Y. , July 1-Aug .",
"articleId": "/m/vinci8/data1/riedel/projects/relation/kb/nyt1/docstore/nyt-2005-2006.backup/1669365.xml.pb",
"relationMentions": [
{"em1Text": "Annandale-on-Hudson", "em2Text": "Bard College", "label": "/location/location/contains"}
],
"entityMentions": [
{"start": 1, "label": "ORGANIZATION", "text": "Bard College"},
{"start": 2, "label": "LOCATION", "text": "Annandale-on-Hudson"}
],
"sentId": "1"
}
Data Fields
default
tokens
: the tokenized text of this example, alist
ofstring
features.spo_list
: the relation triplets (head entity text, relation type, tail entity text), alist
oflist
s containingstring
features.pos_tags
: the part-of-speech tags of this example, alist
ofstring
features.relations
: list of relationsh
: the head entitytext
: the entity text, astring
feature.start
: start index of the head entity, aint32
feature.end
: end index of the head entity, aint32
feature.type
: the entity type, astring
feature.
t
: the tail entitytext
: the entity text, astring
feature.start
: start index of the tail entity, aint32
feature.end
: end index of the tail entity, aint32
feature.type
: the entity type, astring
feature.
type
: relation type, astring
feature.
raw
sentText
: the text of this example, astring
feature.articleId
: the id of the article, astring
feature.relationMentions
: list of relation mentionsem1Text
: the head entity text, astring
feature.em2Text
: the tail entity text, astring
feature.label
: relation type, astring
feature.
entityMentions
: list of entity mentionsstart
: start index of the tail entity, aint32
feature.label
: the entity type, astring
feature.text
: the entity text, astring
feature.
sentId
: index of the sentence, astring
feature
Citation
BibTeX:
@inproceedings{zeng-etal-2018-extracting,
title = "Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism",
author = "Zeng, Xiangrong and
Zeng, Daojian and
He, Shizhu and
Liu, Kang and
Zhao, Jun",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1047",
doi = "10.18653/v1/P18-1047",
pages = "506--514",
abstract = "The relational facts in sentences are often complicated. Different relational triplets may have overlaps in a sentence. We divided the sentences into three types according to triplet overlap degree, including Normal, EntityPairOverlap and SingleEntiyOverlap. Existing methods mainly focus on Normal class and fail to extract relational triplets precisely. In this paper, we propose an end-to-end model based on sequence-to-sequence learning with copy mechanism, which can jointly extract relational facts from sentences of any of these classes. We adopt two different strategies in decoding process: employing only one united decoder or applying multiple separated decoders. We test our models in two public datasets and our model outperform the baseline method significantly.",
}
@article{yu-etal-2019-joint,
author = {Bowen Yu and
Zhenyu Zhang and
Jianlin Su and
Yubin Wang and
Tingwen Liu and
Bin Wang and
Sujian Li},
title = {Joint Extraction of Entities and Relations Based on a Novel Decomposition
Strategy},
journal = {CoRR},
volume = {abs/1909.04273},
year = {2019},
url = {http://arxiv.org/abs/1909.04273},
eprinttype = {arXiv},
eprint = {1909.04273},
timestamp = {Mon, 24 Aug 2020 08:57:29 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1909-04273.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{riedel2010modeling,
title={Modeling relations and their mentions without labeled text},
author={Riedel, Sebastian and Yao, Limin and McCallum, Andrew},
booktitle={Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part III 21},
pages={148--163},
year={2010},
organization={Springer}
}
APA:
- Zeng, X., Zeng, D., He, S., Liu, K., & Zhao, J. (2018, July). Extracting relational facts by an end-to-end neural model with copy mechanism. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 506-514).
- Yu, B., Zhang, Z., Su, J., Wang, Y., Liu, T., Wang, B., & Li, S. (2019). Joint extraction of entities and relations based on a novel decomposition strategy. CoRR, abs/1909.04273. Retrieved from http://arxiv.org/abs/1909.04273
- Riedel, S., Yao, L., & McCallum, A. (2010). Modeling relations and their mentions without labeled text. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part III 21 (pp. 148-163). Springer Berlin Heidelberg.
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