Datasets:

allenai
/

scico

flatten_tokens sequence	flatten_mentions sequence	tokens sequence	doc_ids sequence	metadata sequence	sentences sequence	mentions sequence	relations sequence	id int32 521 741	source stringclasses 3 values	hard_10 bool 2 classes	hard_20 bool 2 classes	curated bool 2 classes
[ "With", "the", "rapid", "development", "of", "the", "network", "technologies", ",", "software", "development", "is", "becoming", "more", "and", "more", "complicated", ".", "Traditional", "software", "engineering", "management", "methods", "based", "on", "Client/Serv...	[ [ 9, 10, 2 ], [ 50, 52, 2 ], [ 85, 86, 2 ], [ 137, 138, 2 ], [ 176, 178, 2 ], [ 218, 221, 2 ], [ 268, 269, 2 ], [ 319, 320, 3 ], [ 419, 421, 3 ], [ 464, 466, 3 ...	[ [ "With", "the", "rapid", "development", "of", "the", "network", "technologies", ",", "software", "development", "is", "becoming", "more", "and", "more", "complicated", ".", "Traditional", "software", "engineering", "management"...	[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53...	{ "title": [ "P2P-Based Software Engineering Management", "Architectural Approaches for HL7-based Health Information Systems Implementation", "Relationship Research Between Communication Activities and Success Indexes in Small and Medium Software Projects", "COMPONENT DEVELOPING AND SOFTWARE REUSE", ...	[ [ [ 0, 18 ], [ 18, 37 ] ], [ [ 0, 17 ], [ 17, 31 ] ], [ [ 0, 13 ], [ 13, 59 ] ], [ [ 0, 13 ], [ 13, 38 ] ], [ [ 0, 20 ],...	[ [ 0, 9, 10, 2 ], [ 1, 13, 15, 2 ], [ 2, 17, 18, 2 ], [ 3, 10, 11, 2 ], [ 4, 11, 13, 2 ], [ 5, 16, 19, 2 ], [ 6, 18, 19, 2 ], [ 7, 5, 6, 3 ], [ 8, 35...	[ [ 8, 12 ], [ 8, 0 ], [ 8, 5 ], [ 8, 10 ], [ 8, 4 ], [ 8, 1 ], [ 8, 9 ], [ 0, 11 ], [ 2, 7 ], [ 2, 6 ], [ 2, 3 ] ]	521	hypernym	false	false	false
[ "It", "communicate", "from", "one", "vehicle", "(", "source", ")", "to", "another", "vehicle", "(", "destination", ")", "through", "different", "vehicles", "(", "intermediate", "nodes", ")", ".", "A", "numbers", "of", "different", "routing", "protocols", "for"...	[ [ 26, 27, 9 ], [ 72, 74, 9 ], [ 147, 149, 9 ], [ 238, 239, 20 ], [ 303, 304, 9 ], [ 346, 348, 16 ], [ 423, 425, 9 ], [ 523, 526, 9 ], [ 581, 583, 23 ], [ 650, 650, ...	[ [ "It", "communicate", "from", "one", "vehicle", "(", "source", ")", "to", "another", "vehicle", "(", "destination", ")", "through", "different", "vehicles", "(", "intermediate", "nodes", ")", ".", "A", "numbers", "o...	[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50 ]	{ "title": [ "Comparison of Stack Implemented AODV (ai-sAODV) with Queue Implemented AODV (ai-dqAODV) for VANET in Signal Fading Scenario", "Energy efficient routing algorithm over cross layer design and enrichment of security in ZigBee network", "A Novel Cross-layer Node-Disjoint Multipath Routing Protoc...	[ [ [ 0, 22 ], [ 22, 39 ], [ 39, 51 ] ], [ [ 0, 12 ], [ 12, 34 ], [ 34, 56 ] ], [ [ 0, 36 ], [ 36, 78 ], [ 78, 99 ] ], [ [...	[ [ 0, 26, 27, 9 ], [ 1, 21, 23, 9 ], [ 2, 40, 42, 9 ], [ 3, 32, 33, 20 ], [ 4, 30, 31, 9 ], [ 5, 28, 30, 16 ], [ 6, 35, 37, 9 ], [ 7, 23, 26, 9 ], [ 8, ...	[ [ 12, 13 ], [ 12, 0 ], [ 12, 8 ], [ 12, 5 ], [ 12, 10 ], [ 12, 14 ], [ 12, 4 ], [ 12, 3 ], [ 9, 2 ], [ 9, 11 ], [ 9, 7 ], [ 9, 16 ], [ 9, 20 ], [ 2, 6 ...	522	hypernym	false	false	false
["In","this","paper",",","we","focus","on","the","feature","pooling","methods","for","scene","charac(...TRUNCATED)	[[21,22,4],[87,88,4],[191,192,4],[272,273,11],[340,341,4],[430,430,4],[503,503,4],[572,574,4],[629,6(...TRUNCATED)	[["In","this","paper",",","we","focus","on","the","feature","pooling","methods","for","scene","chara(...TRUNCATED)	[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,22,22,23,24,25,26,26,27,27,28,29,30,31,3(...TRUNCATED)	{"title":["Feature Pooling in Scene Character Recognition: A Comprehensive Study","Generalizing Pool(...TRUNCATED)	[[[0,16],[16,38],[38,60]],[[0,21],[21,71],[71,96]],[[0,16],[16,52],[52,72]],[[0,27],[27,54],[54,68]](...TRUNCATED)	[[0,21,22,4],[1,27,28,4],[2,35,36,4],[3,44,45,11],[4,44,45,4],[5,75,75,4],[6,57,57,4],[7,50,52,4],[8(...TRUNCATED)	[ [ 4, 12 ], [ 4, 7 ], [ 4, 0 ], [ 7, 8 ], [ 7, 6 ], [ 12, 1 ], [ 12, 9 ], [ 9, 10 ], [ 10, 5 ], [ 5, 3 ], [ 5, 2 ] ]	523	pwc	false	false	false
["In","this","paper","we","focus","on","112","functions","from","the","Python","pandas","library","f(...TRUNCATED)	[[33,34,16],[106,108,16],[151,153,16],[196,198,16],[271,274,17],[322,324,16],[380,383,16],[453,454,1(...TRUNCATED)	[["In","this","paper","we","focus","on","112","functions","from","the","Python","pandas","library","(...TRUNCATED)	[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,3(...TRUNCATED)	{"title":["Neural Inference of API Functions from Input–Output Examples","Trials and Tribulations (...TRUNCATED)	[[[0,28],[28,55],[55,82]],[[0,28],[28,44]],[[0,19],[19,32],[32,40]],[[0,17],[17,40],[40,55]],[[0,33](...TRUNCATED)	[[0,33,34,16],[1,24,26,16],[2,25,27,16],[3,30,32,16],[4,50,53,17],[5,33,35,16],[6,33,36,16],[7,38,39(...TRUNCATED)	[ [ 9, 0 ], [ 0, 2 ], [ 0, 8 ], [ 0, 7 ], [ 0, 3 ], [ 12, 16 ], [ 12, 14 ], [ 14, 13 ], [ 14, 5 ], [ 14, 11 ], [ 16, 17 ], [ 4, 10 ] ]	524	pwc	false	false	false
["Thanks","to","the","widespread","use","of","digital","pathology","and","the","development","of","a(...TRUNCATED)	[[41,42,5],[88,91,10],[197,199,5],[253,255,5],[327,329,5],[447,449,5],[504,506,5],[634,634,6],[715,7(...TRUNCATED)	[["Thanks","to","the","widespread","use","of","digital","pathology","and","the","development","of","(...TRUNCATED)	[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 ]	{"title":["Cell‐type based semantic segmentation of histopathological images using deep convolutio(...TRUNCATED)	[[[0,28],[28,54],[54,84]],[[0,18],[18,54]],[[0,30],[30,63],[63,97]],[[0,13],[13,29],[29,48]],[[0,33](...TRUNCATED)	[[0,41,42,5],[1,4,7,10],[2,59,61,5],[3,18,20,5],[4,44,46,5],[5,27,29,5],[6,26,28,5],[7,76,76,6],[8,3(...TRUNCATED)	[ [ 5, 9 ], [ 5, 8 ], [ 5, 0 ], [ 5, 11 ], [ 5, 10 ], [ 11, 3 ], [ 0, 4 ], [ 0, 7 ], [ 8, 1 ] ]	525	pwc	false	false	false
["This","paper","describes","the","need","and","rationale","for","developing","a","phases","model","(...TRUNCATED)	[[38,38,12],[117,118,11],[196,197,0],[253,255,13],[288,291,20],[352,357,8],[416,419,23],[501,504,17](...TRUNCATED)	[["This","paper","describes","the","need","and","rationale","for","developing","a","phases","model",(...TRUNCATED)	[0,1,2,3,4,5,6,7,8,9,10,10,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,3(...TRUNCATED)	{"title":["Alcohol-Problem Prevention Research Policy: The Need for a Phases Research Model","Automa(...TRUNCATED)	[[[0,18],[18,44],[44,111]],[[0,14],[14,49]],[[0,39],[39,60]],[[0,15],[15,37],[37,53]],[[0,20],[20,72(...TRUNCATED)	[[0,38,38,12],[1,6,7,11],[2,36,37,0],[3,33,35,13],[4,15,18,20],[5,7,12,8],[6,27,30,23],[7,69,72,17],(...TRUNCATED)	[[12,1],[12,3],[12,5],[12,2],[12,7],[12,14],[12,17],[12,23],[12,8],[12,20],[12,13],[12,0],[12,11],[7(...TRUNCATED)	526	pwc	false	false	false
["Among","such","approaches",",","Restricted","Boltzmann","Machine","(","RBM",")","is","one","of","t(...TRUNCATED)	[[31,32,11],[73,74,11],[119,121,11],[210,212,11],[248,250,11],[322,324,11],[396,397,11],[428,429,11](...TRUNCATED)	[["Among","such","approaches",",","Restricted","Boltzmann","Machine","(","RBM",")","is","one","of","(...TRUNCATED)	[0,1,2,3,4,5,6,7,8,9,10,11,11,11,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,28,28,28,28,2(...TRUNCATED)	{"title":["A Robust Restricted Boltzmann Machine for Binary Image Denoising","Non-linear aggregation(...TRUNCATED)	[[[0,21],[21,44],[44,64]],[[0,12],[12,39]],[[0,20],[20,39]],[[0,24],[24,72],[72,84]],[[0,22],[22,33](...TRUNCATED)	[[0,31,32,11],[1,9,10,11],[2,16,18,11],[3,68,70,11],[4,22,24,11],[5,40,42,11],[6,47,48,11],[6,79,80,(...TRUNCATED)	[[5,20],[5,11],[11,22],[11,19],[11,1],[11,23],[11,12],[11,15],[11,3],[11,8],[11,4],[11,10],[11,2],[1(...TRUNCATED)	527	pwc	false	false	false
["We","show","how","effectively","string","kernels","suit","spam","filtering","problem",".","On","th(...TRUNCATED)	[[23,24,2],[93,95,2],[139,141,2],[185,187,2],[247,249,2],[318,319,2],[384,386,2],[443,444,2],[471,47(...TRUNCATED)	[["We","show","how","effectively","string","kernels","suit","spam","filtering","problem",".","On","t(...TRUNCATED)	[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33 ]	{"title":["On email spam filtering using support vector machine","A unified view on association and (...TRUNCATED)	[[[0,11],[11,38],[38,58]],[[0,29],[29,56],[56,77]],[[0,17],[17,40]],[[0,14],[14,45]],[[0,22],[22,39](...TRUNCATED)	[[0,23,24,2],[1,35,37,2],[2,4,6,2],[3,10,12,2],[4,27,29,2],[5,41,42,2],[6,15,17,2],[7,34,35,2],[8,3,(...TRUNCATED)	[ [ 3, 1 ], [ 3, 7 ], [ 3, 5 ], [ 3, 4 ], [ 6, 0 ], [ 2, 8 ] ]	528	pwc	false	false	false
["The","core","of","our","solution","is","to","infer","the","temporal","regularity","from","the","or(...TRUNCATED)	[[72,73,5],[107,108,5],[136,138,5],[214,216,5],[271,272,5],[365,366,5],[438,440,5],[543,545,5],[635,(...TRUNCATED)	[["The","core","of","our","solution","is","to","infer","the","temporal","regularity","from","the","o(...TRUNCATED)	[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,3(...TRUNCATED)	{"title":["Blind video temporal consistency","Synthesizing a 4D Spatio-Angular Consistent Light Fiel(...TRUNCATED)	[[[0,31],[31,75]],[[0,19],[19,37]],[[0,11],[11,40],[40,54]],[[0,30],[30,52],[52,66]],[[0,33],[33,66](...TRUNCATED)	[[0,72,73,5],[1,32,33,5],[2,24,26,5],[3,48,50,5],[4,39,40,5],[5,21,22,5],[6,39,41,5],[7,77,79,5],[8,(...TRUNCATED)	[ [ 0, 7 ], [ 0, 1 ], [ 0, 2 ], [ 0, 9 ], [ 7, 8 ], [ 5, 4 ], [ 5, 6 ] ]	529	pwc	false	false	false
["Genetic","algorithms","have","been","successfully","applied","to","a","wide","variety","of","probl(...TRUNCATED)	[[0,1,7],[64,65,7],[118,119,3],[186,187,3],[234,235,3],[296,297,7],[392,393,7],[431,432,7],[483,485,(...TRUNCATED)	[["Genetic","algorithms","have","been","successfully","applied","to","a","wide","variety","of","prob(...TRUNCATED)	[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,24,24,24,25,26,27,28,29,30,31,32,3(...TRUNCATED)	{"title":["ACTUALITY AND DEVELOPMENTAL TREND FOR GENETIC ALGORITHMS","FEATURE TRANSFORMATION: A GENE(...TRUNCATED)	[[[0,13],[13,23]],[[0,22],[22,44],[44,64]],[[0,17],[17,34],[34,52]],[[0,22],[22,53],[53,70]],[[0,20](...TRUNCATED)	[[0,0,1,7],[1,41,42,7],[2,31,32,3],[3,47,48,3],[4,25,26,3],[5,34,35,7],[6,55,56,7],[7,6,7,7],[8,32,3(...TRUNCATED)	[ [ 19, 10 ], [ 19, 23 ], [ 10, 1 ], [ 6, 14 ], [ 14, 5 ], [ 3, 7 ], [ 7, 22 ], [ 7, 18 ], [ 7, 0 ], [ 7, 20 ], [ 7, 12 ], [ 7, 8 ], [ 7, 25 ] ]	530	pwc	false	false	false

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Dataset Card for SciCo

Dataset Summary

SciCo consists of clusters of mentions in context and a hierarchy over them. The corpus is drawn from computer science papers, and the concept mentions are methods and tasks from across CS. Scientific concepts pose significant challenges: they often take diverse forms (e.g., class-conditional image synthesis and categorical image generation) or are ambiguous (e.g., network architecture in AI vs. systems research). To build SciCo, we develop a new candidate generation approach built on three resources: a low-coverage KB (https://paperswithcode.com/), a noisy hypernym extractor, and curated candidates.

Supported Tasks and Leaderboards

More Information Needed

Languages

The text in the dataset is in English.

Dataset Structure

Data Instances

More Information Needed

Data Fields

flatten_tokens: a single list of all tokens in the topic
flatten_mentions: array of mentions, each mention is represented by [start, end, cluster_id]
tokens: array of paragraphs
doc_ids: doc_id of each paragraph in tokens
metadata: metadata of each doc_id
sentences: sentences boundaries for each paragraph in tokens [start, end]
mentions: array of mentions, each mention is represented by [paragraph_id, start, end, cluster_id]
relations: array of binary relations between cluster_ids [parent, child]
id: id of the topic
hard_10 and hard_20 (only in the test set): flag for 10% or 20% hardest topics based on Levenshtein similarity.
source: source of this topic PapersWithCode (pwc), hypernym or curated.

Data Splits

	Train	Validation	Test
Topic	221	100	200
Documents	9013	4120	8237
Mentions	10925	4874	10424
Clusters	4080	1867	3711
Relations	2514	1747	2379

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

Additional Information

Dataset Curators

This dataset was initially created by Arie Cattan, Sophie Johnson, Daniel Weld, Ido Dagan, Iz Beltagy, Doug Downey and Tom Hope, while Arie was intern at Allen Institute of Artificial Intelligence.

Licensing Information

This dataset is distributed under Apache License 2.0.

Citation Information

@inproceedings{
    cattan2021scico,
    title={SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts},
    author={Arie Cattan and Sophie Johnson and Daniel S. Weld and Ido Dagan and Iz Beltagy and Doug Downey and Tom Hope},
    booktitle={3rd Conference on Automated Knowledge Base Construction},
    year={2021},
    url={https://openreview.net/forum?id=OFLbgUP04nC}
}