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Datasets:
DFKI-SLT
/
cross_ner

Tasks:
Token Classification
Sub-tasks: named-entity-recognition
Languages: English
Multilinguality: monolingual
Size Categories: 10K<n<100K
Language Creators: found
Annotations Creators: expert-generated
Source Datasets: extended|conll2003
ArXiv:
Tags: cross domain ai news music literature politics
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sequence
0
[ "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", "." ]
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[ "Advocates", "of", "procedural", "representations", "were", "mainly", "centered", "at", "MIT", ",", "under", "the", "leadership", "of", "Marvin", "Minsky", "and", "Seymour", "Papert", "." ]
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[ "The", "standard", "interface", "and", "calculator", "interface", "are", "written", "in", "Java", "." ]
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[ "Octave", "helps", "in", "solving", "linear", "and", "nonlinear", "problems", "numerically", ",", "and", "for", "performing", "other", "numerical", "experiments", "using", "a", "that", "is", "mostly", "compatible", "with", "MATLAB", "." ]
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[ "Variants", "of", "the", "back-propagation", "algorithm", "as", "well", "as", "unsupervised", "methods", "by", "Geoff", "Hinton", "and", "colleagues", "at", "the", "University", "of", "Toronto", "can", "be", "used", "to", "train", "deep", ",", "highly", "nonlinear", "neural", "architectures", ",", "{", "{", "cite", "journal" ]
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[ "or", "equivalently", "using", "DCG", "notation", ":" ]
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[ "Self-organizing", "maps", "differ", "from", "other", "artificial", "neural", "networks", "as", "they", "apply", "competitive", "learning", "as", "opposed", "to", "error-correction", "learning", "such", "as", "backpropagation", "with", "gradient", "descent", ")", ",", "and", "in", "the", "sense", "that", "they", "use", "a", "neighborhood", "function", "to", "preserve", "the", "topological", "properties", "of", "the", "input", "space", "." ]
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[ "Since", "the", "early", "1990s", ",", "it", "has", "been", "recommended", "by", "several", "authorities", ",", "including", "the", "Audio", "Engineering", "Society", "that", "measurements", "of", "dynamic", "range", "be", "made", "with", "an", "audio", "signal", "present", ",", "which", "is", "then", "filtered", "out", "in", "the", "noise", "floor", "measurement", "used", "in", "determining", "dynamic", "range", ".", "This", "avoids", "questionable", "measurements", "based", "on", "the", "use", "of", "blank", "media", ",", "or", "muting", "circuits", "." ]
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[ "The", "technique", "used", "in", "creating", "eigenfaces", "and", "using", "them", "for", "recognition", "is", "also", "used", "outside", "of", "face", "recognition", ":", "handwriting", "recognition", ",", "lip", "reading", ",", "voice", "recognition", ",", "sign", "language", "/", "hand", "gestures", "interpretation", "and", "medical", "imaging", "analysis", "." ]
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[ "The", "National", "Science", "Foundation", "was", "an", "umbrella", "for", "the", "National", "Aeronautics", "and", "Space", "Administration", "(", "NASA", ")", ",", "the", "US", "Department", "of", "Energy", ",", "the", "US", "Department", "of", "Commerce", "NIST", ",", "the", "US", "Department", "of", "Defense", ",", "Defense", "Advanced", "Research", "Projects", "Agency", "(", "DARPA", ")", ",", "and", "the", "Office", "of", "Naval", "Research", "coordinated", "studies", "to", "inform", "strategic", "planners", "in", "their", "deliberations", "." ]
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[ "A", "fast", "method", "for", "computing", "maximum", "likelihood", "estimates", "for", "the", "probit", "model", "was", "proposed", "by", "Ronald", "Fisher", "as", "an", "appendix", "to", "Bliss", "'", "work", "in", "1935", "." ]
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[ "Several", "of", "these", "programs", "are", "available", "online", ",", "such", "as", "Google", "Translate", "and", "the", "SYSTRAN", "system", "that", "powers", "AltaVista", "'s", "BabelFish", "(", "now", "Yahoo", "'s", "Babelfish", "as", "of", "9", "May", "2008", ")", "." ]
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[ "In", "2002", "Hutter", ",", "with", "Jürgen", "Schmidhuber", "and", "Shane", "Legg", ",", "developed", "and", "published", "a", "mathematical", "theory", "of", "artificial", "general", "intelligence", "based", "on", "idealised", "intelligent", "agents", "and", "reward-motivated", "reinforcement", "learning", "." ]
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[ "The", "most", "common", "way", "is", "using", "the", "so-called", "ROUGE", "(", "Recall-Oriented", "Understudy", "for", "Gisting", "Evaluation", ")", "measure", "." ]
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[ "RapidMiner", "provides", "learning", "schemes", ",", "models", "and", "algorithms", "and", "can", "be", "extended", "using", "R", "and", "Python", "scripts", ".", "David", "Norris", ",", "Bloor", "Research", ",", "November", "13", ",", "2013", "." ]
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[ "tity", "contains", "a", "collection", "of", "visualization", "tools", "and", "algorithms", "for", "data", "analysis", "and", "predictive", "modeling", ",", "together", "with", "graphical", "user", "interfaces", "for", "easy", "access", "to", "these", "functions.", "but", "the", "more", "recent", "fully", "Java", "-based", "version", "(", "Weka", "3", ")", ",", "for", "which", "development", "started", "in", "1997", ",", "is", "now", "used", "in", "many", "different", "application", "areas", ",", "in", "particular", "for", "educational", "purposes", "and", "research", "." ]
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[ "To", "allow", "for", "multiple", "entities", ",", "a", "separate", "Hinge", "loss", "is", "computed", "for", "each", "capsule", "." ]
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[ "With", "the", "emergence", "of", "conversational", "assistants", "such", "as", "Apple", "'s", "Siri", ",", "Amazon", "Alexa", ",", "Google", "Assistant", ",", "Microsoft", "Cortana", ",", "and", "Samsung", "'s", "Bixby", ",", "Voice", "Portals", "can", "now", "be", "accessed", "through", "mobile", "devices", "and", "Far", "Field", "voice", "smart", "speakers", "such", "as", "the", "Amazon", "Echo", "and", "Google", "Home", "." ]
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[ "It", "also", "created", "flexible", "intelligent", "AGV", "applications", ",", "designing", "the", "Motivity", "control", "system", "used", "by", "RMT", "Robotics", "to", "develop", "its", "ADAM", "iAGV", "(", "Self-Guided", "Vehicle", ")", ",", "used", "for", "complex", "pick", "and", "place", "operations", ",", "in", "conjunction", "with", "gantry", "systems", "and", "industrial", "robot", "arms", ",", "used", "in", "first-tier", "auto", "supply", "factories", "to", "move", "products", "from", "process", "to", "process", "in", "non-linear", "layouts", "." ]
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79
[ "The", "parameters", "β", "are", "typically", "estimated", "by", "maximum", "likelihood", "." ]
[ 0, 0, 0, 0, 0, 0, 0, 39, 40, 0 ]
80
[ "The", "information", "retrieval", "metrics", "such", "as", "precision", "and", "recall", "or", "DCG", "are", "useful", "to", "assess", "the", "quality", "of", "a", "recommendation", "method", "." ]
[ 0, 71, 72, 0, 0, 0, 39, 0, 39, 0, 39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
81
[ "A", "typical", "factory", "contains", "hundreds", "of", "industrial", "robot", "s", "working", "on", "fully", "automated", "production", "lines", ",", "with", "one", "robot", "for", "every", "ten", "human", "workers", "." ]
[ 0, 0, 0, 0, 0, 0, 59, 60, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
82
[ "Over", "the", "past", "decade", ",", "PCNNs", "have", "been", "used", "in", "a", "variety", "of", "image", "processing", "applications", ",", "including", ":", "image", "segmentation", ",", "feature", "generation", ",", "face", "extraction", ",", "motion", "detection", ",", "region", "growing", ",", "and", "noise", "reduction", "." ]
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83
[ "Xu", "has", "published", "more", "than", "50", "papers", "at", "international", "conferences", "and", "in", "journals", "in", "the", "field", "of", "computer", "vision", "and", "won", "the", "Best", "Paper", "Award", "at", "the", "international", "conference", "on", "Non-Photorealistic", "Rendering", "and", "Animation", "(", "NPAR", ")", "2012", "and", "the", "Best", "Reviewer", "Award", "at", "the", "international", "conferences", "Asian", "Conference", "on", "Computer", "Vision", "ACCV", "2012", "and", "International", "Conference", "on", "Computer", "Vision", "(", "ICCV", ")", "2015", "." ]
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84
[ "CycL", "in", "computer", "science", "and", "artificial", "intelligence", "is", "an", "ontology", "language", "used", "by", "Doug", "Lenat", "'s", "Cyc", "artificial", "project", "." ]
[ 61, 0, 31, 32, 0, 31, 32, 0, 0, 41, 42, 0, 0, 65, 66, 0, 41, 42, 42, 0 ]
85
[ "Also", "in", "regression", "analysis", ",", "mean", "squared", "error", ",", "often", "referred", "to", "as", "mean", "squared", "prediction", "error", "or", "out-of-sample", "mean", "squared", "error", ",", "can", "refer", "to", "the", "mean", "value", "of", "the", "squared", "deviations", "of", "the", "predictions", "from", "the", "TRUE", "values", ",", "over", "an", "out-of-sample", "test", "space", ",", "generated", "by", "a", "model", "estimated", "over", "a", "particular", "sample", "space", "." ]
[ 0, 0, 71, 72, 0, 39, 40, 40, 0, 0, 0, 0, 0, 39, 40, 40, 40, 0, 39, 40, 40, 40, 0, 0, 0, 0, 0, 0, 0, 0, 0, 41, 42, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
86
[ "As", "for", "the", "results", ",", "the", "C-HOG", "and", "R-HOG", "block", "descriptors", "perform", "comparably", ",", "with", "the", "C-HOG", "descriptors", "maintaining", "a", "slight", "advantage", "in", "the", "detection", "miss", "rate", "at", "fixed", "FALSE", "positive", "rate", "s", "across", "both", "data", "sets", "." ]
[ 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39, 40, 40, 0, 0, 0, 0, 0, 0 ]
87
[ "Popular", "recognition", "algorithms", "include", "principal", "component", "analysis", "using", "eigenface", "s", ",", "linear", "discriminant", "analysis", ",", "Elastic", "matching", "using", "the", "Fisherface", "algorithm", ",", "the", "hidden", "Markov", "model", ",", "the", "multilinear", "subspace", "learning", "using", "tensor", "representation", ",", "and", "the", "neuronal", "motivated", "dynamic", "link", "matching", "." ]
[ 0, 0, 0, 0, 5, 6, 6, 0, 41, 0, 0, 5, 6, 6, 0, 5, 6, 0, 0, 5, 6, 0, 0, 5, 6, 6, 0, 0, 5, 6, 6, 0, 41, 42, 0, 0, 0, 0, 0, 5, 6, 6, 0 ]
88
[ "Beginning", "at", "the", "2019", "Toronto", "International", "Film", "Festival", ",", "films", "may", "now", "be", "restricted", "from", "screening", "at", "Scotiabank", "Theatre", "Toronto", "-", "one", "of", "the", "festival", "'s", "main", "venues", "-", "and", "screened", "elsewhere", "(", "such", "as", "TIFF", "Bell", "Lightbox", "and", "other", "local", "cinemas", ")", "if", "distributed", "by", "a", "service", "such", "as", "Netflix", "." ]
[ 0, 0, 0, 41, 42, 42, 42, 42, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35, 36, 36, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35, 36, 36, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 49, 0 ]
89
[ "Unimation", "purchased", "Victor", "Scheinman", "'", "s", "Vicarm", "Inc.", "in", "1977", ",", "and", "with", "Scheinman", "'s", "help", ",", "the", "company", "created", "and", "began", "producing", "the", "Programmable", "Universal", "Machine", "for", "Assembly", ",", "a", "new", "model", "of", "robotic", "arm", ",", "and", "using", "Scheinman", "'s", "cutting-edge", "VAL", "programming", "language", "." ]
[ 49, 0, 65, 66, 0, 0, 49, 50, 0, 0, 0, 0, 0, 65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59, 60, 60, 60, 60, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 65, 0, 0, 61, 62, 62, 0 ]
90
[ "J48", "is", "an", "open", "source", "Java", "implementation", "of", "the", "C4.5", "algorithm", "in", "the", "Weka", "data", "mining", "tool", "." ]
[ 59, 0, 0, 0, 0, 61, 0, 0, 0, 5, 6, 0, 0, 59, 60, 60, 60, 0 ]
91
[ "The", "2004", "SSIM", "paper", "has", "been", "cited", "over", "20,000", "times", "according", "to", "Google", "Scholar", ",", "It", "also", "received", "the", "IEEE", "Signal", "Processing", "Society", "Sustained", "Impact", "Award", "for", "2016", ",", "indicative", "of", "a", "paper", "having", "an", "unusually", "high", "impact", "for", "at", "least", "10", "years", "following", "its", "publication", "." ]
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92
[ "The", "speech", "synthesis", "is", "verging", "on", "being", "completely", "indistinguishable", "from", "a", "real", "human", "'s", "voice", "with", "the", "2016", "introduction", "of", "the", "voice", "editing", "and", "generation", "software", "Adobe", "Voco", ",", "a", "prototype", "slated", "to", "be", "a", "part", "of", "the", "Adobe", "Creative", "Suite", "and", "DeepMind", "WaveNet", ",", "a", "prototype", "from", "Google", "." ]
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93
[ "Poggio", "is", "an", "honorary", "member", "of", "the", "Neuroscience", "Research", "Program", ",", "a", "member", "of", "the", "American", "Academy", "of", "Arts", "and", "Sciences", "and", "a", "founding", "fellow", "of", "AAAI", "and", "a", "founding", "member", "of", "the", "McGovern", "Institute", "for", "Brain", "Research", "." ]
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94
[ "During", "the", "1990s", ",", "encouraged", "by", "successes", "in", "speech", "recognition", "and", "speech", "synthesis", ",", "research", "began", "into", "speech", "translation", "with", "the", "development", "of", "the", "German", "Verbmobil", "project", "." ]
[ 0, 0, 0, 0, 0, 0, 0, 0, 71, 72, 0, 71, 72, 0, 0, 0, 0, 71, 72, 0, 0, 0, 0, 0, 41, 41, 42, 0 ]
95
[ "In", "1999", ",", "Felix", "Gers", "and", "his", "advisor", "Jürgen", "Schmidhuber", "and", "Fred", "Cummins", "introduced", "the", "forget", "gate", "(", "also", "called", "keep", "gate", ")", "into", "LSTM", "architecture", "," ]
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96
[ "In", "digital", "signal", "processing", "and", "information", "theory", ",", "the", "normalized", "sinc", "function", "is", "commonly", "defined", "for", "by" ]
[ 0, 31, 32, 32, 0, 31, 32, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0 ]
97
[ "The", "term", "computational", "linguistics", "itself", "was", "first", "coined", "by", "David", "Hays", ",", "a", "founding", "member", "of", "both", "the", "Association", "for", "Computational", "Linguistics", "and", "the", "International", "Committee", "on", "Computational", "Linguistics", "(", "ICCL", ")", "." ]
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98
[ "59", ",", "pp.", "2547-2553", ",", "Oct.", "2011", "In", "one", "dimensional", "polynomial-based", "memory", "(", "or", "memoryless", ")", "DPD", ",", "in", "order", "to", "solve", "for", "the", "digital", "pre-distorter", "polynomials", "coefficients", "and", "minimize", "the", "mean", "squared", "error", "(", "MSE", ")", ",", "the", "distorted", "output", "of", "the", "nonlinear", "system", "must", "be", "over-sampled", "at", "a", "rate", "that", "enables", "the", "capture", "of", "the", "nonlinear", "products", "of", "the", "order", "of", "the", "digital", "pre-distorter", "." ]
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99
[ "Boris", "Katz", ",", "(", "born", "October", "5", ",", "1947", ",", "Chișinău", ",", "Moldavian", "SSR", ",", "Soviet", "Union", ",", "(", "now", "Chișinău", ",", "Moldova", ")", ")", "is", "a", "principal", "American", "research", "scientist", "(", "computer", "scientist", ")", "at", "the", "MIT", "Computer", "Science", "and", "Artificial", "Intelligence", "Laboratory", "at", "the", "Massachusetts", "Institute", "of", "Technology", "in", "Cambridge", "and", "head", "of", "the", "Laboratory", "'s", "InfoLab", "Group", "." ]
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Dataset Card for CrossRE

Dataset Summary

CrossNER is a fully-labeled collected of named entity recognition (NER) data spanning over five diverse domains (Politics, Natural Science, Music, Literature, and Artificial Intelligence) with specialized entity categories for different domains. Additionally, CrossNER also includes unlabeled domain-related corpora for the corresponding five domains.

For details, see the paper: CrossNER: Evaluating Cross-Domain Named Entity Recognition

Supported Tasks and Leaderboards

More Information Needed

Languages

The language data in CrossNER is in English (BCP-47 en)

Dataset Structure

Data Instances

conll2003

  • Size of downloaded dataset files: 2.69 MB
  • Size of the generated dataset: 5.26 MB

An example of 'train' looks as follows:

{
  "id": "0", 
  "tokens": ["EU", "rejects", "German", "call", "to", "boycott", "British", "lamb", "."], 
  "ner_tags": [49, 0, 41, 0, 0, 0, 41, 0, 0]
}

politics

  • Size of downloaded dataset files: 0.72 MB
  • Size of the generated dataset: 1.04 MB

An example of 'train' looks as follows:

{
  "id": "0", 
  "tokens": ["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_tags": [0, 0, 0, 0, 0, 0, 0, 0, 55, 56, 0, 0, 0, 0, 0, 55, 56, 56, 56, 56, 56, 0, 55, 56, 56, 56, 56, 0]
}

science

  • Size of downloaded dataset files: 0.49 MB
  • Size of the generated dataset: 0.73 MB

An example of 'train' looks as follows:

{
  "id": "0", 
  "tokens": ["They", "may", "also", "use", "Adenosine", "triphosphate", ",", "Nitric", "oxide", ",", "and", "ROS", "for", "signaling", "in", "the", "same", "ways", "that", "animals", "do", "."], 
  "ner_tags": [0, 0, 0, 0, 15, 16, 0, 15, 16, 0, 0, 15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}

music

  • Size of downloaded dataset files: 0.41 MB
  • Size of the generated dataset: 0.65 MB

An example of 'train' looks as follows:

{
  "id": "0", 
  "tokens": ["In", "2003", ",", "the", "Stade", "de", "France", "was", "the", "primary", "site", "of", "the", "2003", "World", "Championships", "in", "Athletics", "."], 
  "ner_tags": [0, 0, 0, 0, 35, 36, 36, 0, 0, 0, 0, 0, 0, 29, 30, 30, 30, 30, 0]
}

literature

  • Size of downloaded dataset files: 0.33 MB
  • Size of the generated dataset: 0.58 MB

An example of 'train' looks as follows:

{
  "id": "0",
  "tokens": ["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_tags": [0, 0, 0, 0, 0, 0, 0, 51, 52, 52, 0, 0, 21, 22, 0, 0, 0, 77, 78, 0, 77, 0, 0, 0, 0, 0, 77, 78, 0, 77, 0, 0, 0, 21, 0, 21, 0, 0, 41, 0, 0, 0, 0, 0, 0, 51, 52, 0, 0, 41, 0, 0, 0, 0, 0, 51, 0, 0]
}

ai

  • Size of downloaded dataset files: 0.29 MB
  • Size of the generated dataset: 0.48 MB

An example of 'train' looks as follows:

{
  "id": "0", 
  "tokens": ["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_tags": [0, 0, 0, 59, 60, 60, 0, 0, 0, 0, 31, 32, 0, 71, 72, 0, 71, 72, 0, 0, 0, 71, 72, 72, 0, 0, 31, 32, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}

Data Fields

The data fields are the same among all splits.

  • id: the instance id of this sentence, a string feature.
  • tokens: the list of tokens of this sentence, a list of string features.
  • ner_tags: the list of entity tags, a list of classification labels.
{"O": 0, "B-academicjournal": 1, "I-academicjournal": 2, "B-album": 3, "I-album": 4, "B-algorithm": 5, "I-algorithm": 6, "B-astronomicalobject": 7, "I-astronomicalobject": 8, "B-award": 9, "I-award": 10, "B-band": 11, "I-band": 12, "B-book": 13, "I-book": 14, "B-chemicalcompound": 15, "I-chemicalcompound": 16, "B-chemicalelement": 17, "I-chemicalelement": 18, "B-conference": 19, "I-conference": 20, "B-country": 21, "I-country": 22, "B-discipline": 23, "I-discipline": 24, "B-election": 25, "I-election": 26, "B-enzyme": 27, "I-enzyme": 28, "B-event": 29, "I-event": 30, "B-field": 31, "I-field": 32, "B-literarygenre": 33, "I-literarygenre": 34, "B-location": 35, "I-location": 36, "B-magazine": 37, "I-magazine": 38, "B-metrics": 39, "I-metrics": 40, "B-misc": 41, "I-misc": 42, "B-musicalartist": 43, "I-musicalartist": 44, "B-musicalinstrument": 45, "I-musicalinstrument": 46, "B-musicgenre": 47, "I-musicgenre": 48, "B-organisation": 49, "I-organisation": 50, "B-person": 51, "I-person": 52, "B-poem": 53, "I-poem": 54, "B-politicalparty": 55, "I-politicalparty": 56, "B-politician": 57, "I-politician": 58, "B-product": 59, "I-product": 60, "B-programlang": 61, "I-programlang": 62, "B-protein": 63, "I-protein": 64, "B-researcher": 65, "I-researcher": 66, "B-scientist": 67, "I-scientist": 68, "B-song": 69, "I-song": 70, "B-task": 71, "I-task": 72, "B-theory": 73, "I-theory": 74, "B-university": 75, "I-university": 76, "B-writer": 77, "I-writer": 78}

Data Splits

Train Dev Test
conll2003 14,987 3,466 3,684
politics 200 541 651
science 200 450 543
music 100 380 456
literature 100 400 416
ai 100 350 431

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

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

Citation Information 

@article{liu2020crossner,
      title={CrossNER: Evaluating Cross-Domain Named Entity Recognition}, 
      author={Zihan Liu and Yan Xu and Tiezheng Yu and Wenliang Dai and Ziwei Ji and Samuel Cahyawijaya and Andrea Madotto and Pascale Fung},
      year={2020},
      eprint={2012.04373},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Contributions

Thanks to @phucdev for adding this dataset.

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Repository:
CrossNER
Paper:
CrossNER: Evaluating Cross-Domain Named Entity Recognition
Size of downloaded dataset files:
4.94 MB
Size of the auto-converted Parquet files:
2.33 MB
Number of rows:
26,071