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ai-train-1
[ "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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ai-train-2
[ "Advocates", "of", "procedural", "representations", "were", "mainly", "centered", "at", "MIT", ",", "under", "the", "leadership", "of", "Marvin", "Minsky", "and", "Seymour", "Papert", "." ]
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ai-train-3
[ "The", "standard", "interface", "and", "calculator", "interface", "are", "written", "in", "Java", "." ]
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[]
ai-train-4
[ "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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ai-train-5
[ "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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ai-train-6
[ "or", "equivalently", "using", "DCG", "notation", ":" ]
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ai-train-7
[ "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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ai-train-8
[ "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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ai-train-9
[ "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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ai-train-10
[ "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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ai-train-11
[ "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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ai-train-12
[ "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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ai-train-13
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ai-train-14
[ "The", "most", "common", "way", "is", "using", "the", "so-called", "ROUGE", "(", "Recall-Oriented", "Understudy", "for", "Gisting", "Evaluation", ")", "measure", "." ]
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ai-train-15
[ "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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ai-train-16
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ai-train-17
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ai-train-18
[ "To", "allow", "for", "multiple", "entities", ",", "a", "separate", "Hinge", "loss", "is", "computed", "for", "each", "capsule", "." ]
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[]
ai-train-19
[ "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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ai-train-20
[ "Examples", "of", "supervised", "learning", "are", "Naive", "Bayes", "classifier", ",", "Support", "vector", "machine", ",", "mixtures", "of", "Gaussians", ",", "and", "network", "." ]
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ai-train-21
[ "One", "can", "use", "the", "OSD", "algorithm", "to", "derive", "math", "O", "(", "\\", "sqrt", "{", "T", "}", ")", "/", "math", "regret", "bounds", "for", "the", "online", "version", "of", "Support", "vector", "machine", "for", "classification", ",", "which", "use", "the", "hinge", "loss", "math", "v", "_", "t", "(", "w", ")", "=", "\\", "max", "\\", "{", "0", ",", "1", "-", "y", "_", "t", "(", "w", "\\", "cdot", "x", "_", "t", ")", "\\", "}", "/", "math" ]
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ai-train-22
[ "Applications", "include", "object", "recognition", ",", "robotic", "mapping", "and", "navigation", ",", "image", "stitching", ",", "3D", "modeling", ",", "gesture", "recognition", ",", "video", "tracking", ",", "individual", "identification", "of", "wildlife", "and", "match", "moving", "." ]
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[]
ai-train-23
[ "A", "number", "of", "groups", "and", "companies", "are", "researching", "pose", "estimation", ",", "including", "groups", "at", "Brown", "University", ",", "Carnegie", "Mellon", "University", ",", "MPI", "Saarbruecken", ",", "Stanford", "University", ",", "the", "University", "of", "California", ",", "San", "Diego", ",", "the", "University", "of", "Toronto", ",", "the", "École", "Centrale", "Paris", ",", "ETH", "Zurich", ",", "National", "University", "of", "Sciences", "and", "Technology", "(", "NUST", ")", ",", "and", "the", "University", "of", "California", ",", "Irvine", "." ]
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ai-train-24
[ "Sigmoid", "function", "Cross", "entropy", "loss", "is", "used", "for", "predicting", "K", "independent", "probability", "values", "in", "math", "0,1", "/", "math", "." ]
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[]
ai-train-25
[ "He", "held", "the", "Johann", "Bernoulli", "Chair", "of", "Mathematics", "and", "Informatics", "at", "the", "University", "of", "Groningen", "in", "the", "Netherlands", ",", "and", "the", "Toshiba", "Endowed", "Chair", "at", "the", "Tokyo", "Institute", "of", "Technology", "in", "Japan", "before", "becoming", "Professor", "at", "Cambridge", "." ]
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ai-train-26
[ "Another", "technique", "particularly", "used", "for", "recurrent", "neural", "network", "s", "is", "the", "long", "short-term", "memory", "(", "LSTM", ")", "network", "of", "1997", "by", "Sepp", "Hochreiter", "&", "Jürgen", "Schmidhuber", "." ]
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ai-train-27
[ "The", "inclusion", "of", "a", "C", "+", "+", "interpreter", "(", "CINT", "until", "version", "5.34", ",", "Cling", "from", "version", "6", ")", "makes", "this", "package", "very", "versatile", "as", "it", "can", "be", "used", "in", "interactive", ",", "scripted", "and", "compiled", "modes", "in", "a", "manner", "similar", "to", "commercial", "products", "like", "MATLAB", "." ]
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ai-train-28
[ "Voice", "user", "interfaces", "that", "interpret", "and", "manage", "conversational", "state", "are", "challenging", "to", "design", "due", "to", "the", "inherent", "difficulty", "of", "integrating", "complex", "natural", "language", "processing", "tasks", "like", "coreference", "resolution", ",", "named-entity", "recognition", ",", "information", "retrieval", ",", "and", "dialog", "management", "." ]
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ai-train-29
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ai-train-30
[ "Modern", "Windows", "desktop", "systems", "can", "use", "SAPI", "4", "and", "SAPI", "5", "components", "to", "support", "speech", "synthesis", "and", "speech", "." ]
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ai-train-31
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ai-train-32
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ai-train-33
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ai-train-34
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ai-train-35
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ai-train-36
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ai-train-37
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ai-train-38
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ai-train-39
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ai-train-40
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[]
ai-train-41
[ "Speech", "recognition", "and", "speech", "synthesis", "deal", "with", "how", "spoken", "language", "can", "be", "understood", "or", "created", "using", "computers", "." ]
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[]
ai-train-42
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ai-train-43
[ "Some", "less", "widely", "spoken", "languages", "use", "the", "open-source", "eSpeak", "synthesizer", "for", "their", "speech", ";", "producing", "a", "robotic", ",", "awkward", "voice", "that", "may", "be", "difficult", "to", "understand", "." ]
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[]
ai-train-44
[ "Although", "used", "mainly", "by", "statisticians", "and", "other", "practitioners", "requiring", "an", "environment", "for", "statistical", "computation", "and", "software", "development", ",", "R", "can", "also", "operate", "as", "a", "general", "matrix", "calculation", "toolbox", "-", "with", "performance", "benchmarks", "comparable", "to", "GNU", "Octave", "or", "MATLAB", "." ]
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ai-train-45
[ "Heterodyning", "is", "a", "signal", "processing", "technique", "invented", "by", "Canadian", "inventor-engineer", "Reginald", "Fessenden", "that", "creates", "new", "frequencies", "by", "combining", "mixing", "two", "frequencies", "." ]
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ai-train-46
[ "Several", "other", "features", "that", "helped", "put", "3D", "back", "on", "the", "map", "that", "month", "were", "the", "John", "Wayne", "feature", "Hondo", "(", "distributed", "by", "Warner", "Bros.", ")", ",", "Columbia", "'s", "Miss", "Sadie", "Thompson", "with", "Rita", "Hayworth", ",", "and", "Paramount", "'s", "Money", "From", "Home", "with", "Dean", "Martin", "and", "Jerry", "Lewis", "." ]
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ai-train-47
[ "DeepFace", "is", "a", "deep", "learning", "facial", "recognition", "system", "created", "by", "a", "research", "group", "at", "Facebook", "." ]
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ai-train-48
[ "Geometry", "processing", "is", "a", "common", "research", "topic", "at", "SIGGRAPH", ",", "the", "premier", "computer", "graphics", "academic", "conference", ",", "and", "the", "main", "topic", "of", "the", "annual", "Symposium", "on", "Geometry", "Processing", "." ]
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ai-train-49
[ "Feature", "extraction", "and", "dimension", "reduction", "can", "be", "combined", "in", "one", "step", "using", "Principal", "Component", "Analysis", "(", "PCA", ")", ",", "linear", "discriminant", "analysis", "(", "LDA", ")", ",", "or", "canonical", "correlation", "analysis", "(", "CCA", ")", "techniques", "as", "a", "pre-processing", "step", ",", "followed", "by", "clustering", "by", "k", "-NN", "on", "feature", "vectors", "in", "reduced-dimension", "space", "." ]
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ai-train-50
[ "Artificial", "neural", "networks", "are", "computational", "models", "that", "excel", "at", "machine", "learning", "and", "pattern", "recognition", "." ]
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ai-train-51
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ai-train-54
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ai-train-57
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ai-train-60
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ai-train-61
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ai-train-62
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ai-train-64
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ai-train-65
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ai-train-66
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ai-train-67
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ai-train-68
[ "Libraries", "written", "in", "Perl", ",", "Java", ",", "ActiveX", "or", ".NET", "can", "be", "directly", "called", "from", "MATLAB", "," ]
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ai-train-69
[ "The", "task", "of", "recognizing", "named", "entities", "in", "text", "is", "Named", "Entity", "Recognition", "while", "the", "task", "of", "determining", "the", "identity", "of", "the", "named", "entities", "mentioned", "in", "text", "is", "called", "Entity", "Linking", "." ]
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ai-train-70
[ "The", "sigmoid", "function", "s", "and", "derivatives", "used", "in", "the", "package", "were", "originally", "included", "in", "the", "package", ",", "from", "version", "0.8.0", "onwards", ",", "these", "were", "released", "in", "a", "separate", "R", "package", "sigmoid", ",", "with", "the", "intention", "to", "enable", "more", "general", "use", "." ]
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ai-train-71
[ "Logo", "was", "created", "in", "1967", "at", "Bolt", ",", "Beranek", "and", "Newman", "(", "BBN", ")", ",", "a", "Cambridge", ",", "Massachusetts", "research", "firm", ",", "by", "Wally", "Feurzeig", ",", "Cynthia", "Solomon", ",", "and", "Seymour", "Papert", "." ]
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ai-train-72
[ "Neuroevolution", "is", "commonly", "used", "as", "part", "of", "the", "reinforcement", "learning", "paradigm", ",", "and", "it", "can", "be", "contrasted", "with", "conventional", "deep", "learning", "techniques", "that", "use", "gradient", "descent", "on", "a", "neural", "network", "with", "a", "fixed", "topology", "." ]
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ai-train-73
[ "If", "we", "use", "least", "squares", "to", "fit", "a", "function", "in", "the", "form", "of", "a", "hyperplane", "ŷ", "=", "a", "+", "β", "supT", "/", "sup", "x", "to", "the", "data", "(", "x", "sub", "i", "/", "sub", ",", "y", "sub", "i", "/", "sub", ")", "sub", "1", "≤", "i", "≤", "n", "/", "sub", ",", "we", "could", "then", "assess", "the", "fit", "using", "the", "mean", "squared", "error", "(", "MSE", ")", "." ]
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ai-train-74
[ "The", "company", "has", "international", "locations", "in", "Australia", ",", "Brazil", ",", "Canada", ",", "China", ",", "Germany", ",", "India", ",", "Italy", ",", "Japan", ",", "Korea", ",", "Lithuania", ",", "Poland", ",", "Malaysia", ",", "the", "Philippines", ",", "Russia", ",", "Singapore", ",", "South", "Africa", ",", "Spain", ",", "Taiwan", ",", "Thailand", ",", "Turkey", "and", "the", "United", "Kingdom", "." ]
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[]
ai-train-75
[ "He", "holds", "a", "D.Sc.", "degree", "in", "electrical", "and", "computer", "engineering", "(", "2000", ")", "from", "Inria", "and", "the", "University", "of", "Nice", "Sophia", "Antipolis", ",", "and", "has", "held", "permanent", "positions", "at", "Siemens", "Corporate", "Technology", ",", "École", "des", "ponts", "ParisTech", "as", "well", "as", "visiting", "positions", "at", "Rutgers", "University", ",", "Yale", "University", "and", "University", "of", "Houston", "." ]
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ai-train-76
[ "Licensing", "the", "original", "patent", "awarded", "to", "inventor", "George", "Devol", ",", "Engelberger", "developed", "the", "first", "industrial", "robot", "in", "the", "United", "States", ",", "the", "Unimate", ",", "in", "the", "1950s", "." ]
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ai-train-77
[ "The", "input", "is", "called", "speech", "recognition", "and", "the", "output", "is", "called", "speech", "synthesis", "." ]
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[]
ai-train-78
[ "Descendants", "of", "the", "CLIPS", "language", "include", "Jess", "(", "rule-based", "portion", "of", "CLIPS", "rewritten", "in", "Java", ",", "it", "later", "grew", "up", "in", "different", "direction", ")", ",", "JESS", "was", "originally", "inspired" ]
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ai-train-79
[ "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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ai-train-80
[ "The", "parameters", "β", "are", "typically", "estimated", "by", "maximum", "likelihood", "." ]
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[]
ai-train-81
[ "The", "information", "retrieval", "metrics", "such", "as", "precision", "and", "recall", "or", "DCG", "are", "useful", "to", "assess", "the", "quality", "of", "a", "recommendation", "method", "." ]
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ai-train-82
[ "A", "typical", "factory", "contains", "hundreds", "of", "industrial", "robot", "s", "working", "on", "fully", "automated", "production", "lines", ",", "with", "one", "robot", "for", "every", "ten", "human", "workers", "." ]
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[]
ai-train-83
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ai-train-84
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ai-train-85
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ai-train-86
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ai-train-87
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ai-train-88
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ai-train-89
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ai-train-90
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ai-train-91
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ai-train-92
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ai-train-93
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ai-train-94
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ai-train-95
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ai-train-96
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ai-train-97
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[ { "id_1-start": 9, "id_1-end": 11, "id_2-start": 1, "id_2-end": 3, "relation-type": "part-of", "Exp": "", "Un": false, "SA": false }, { "id_1-start": 9, "id_1-end": 11, "id_2-start": 5, "id_2-end": 6, "relation-type": "part-of", "Exp": "", "Un": false, "SA": false } ]
ai-train-98
[ "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", ")", "." ]
[ { "id-start": 2, "id-end": 3, "entity-type": "field" }, { "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 }, { "id_1-start": 9, "id_1-end": 10, "id_2-start": 18, "id_2-end": 21, "relation-type": "role", "Exp": "", "Un": false, "SA": false }, { "id_1-start": 9, "id_1-end": 10, "id_2-start": 24, "id_2-end": 28, "relation-type": "role", "Exp": "", "Un": false, "SA": false }, { "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
[ "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", "." ]
[ { "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" } ]
[ { "id_1-start": 35, "id_1-end": 35, "id_2-start": 31, "id_2-end": 33, "relation-type": "named", "Exp": "", "Un": false, "SA": false } ]
ai-train-100
[ "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", "." ]
[ { "id-start": 0, "id-end": 1, "entity-type": "researcher" }, { "id-start": 10, "id-end": 10, "entity-type": "location" }, { "id-start": 12, "id-end": 13, "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" } ]
[ { "id_1-start": 0, "id_1-end": 1, "id_2-start": 10, "id_2-end": 10, "relation-type": "physical", "Exp": "", "Un": false, "SA": false }, { "id_1-start": 0, "id_1-end": 1, "id_2-start": 46, "id_2-end": 49, "relation-type": "physical", "Exp": "", "Un": false, "SA": false }, { "id_1-start": 0, "id_1-end": 1, "id_2-start": 58, "id_2-end": 59, "relation-type": "role", "Exp": "", "Un": false, "SA": false }, { "id_1-start": 10, "id_1-end": 10, "id_2-start": 12, "id_2-end": 13, "relation-type": "physical", "Exp": "", "Un": false, "SA": false }, { "id_1-start": 12, "id_1-end": 13, "id_2-start": 15, "id_2-end": 16, "relation-type": "physical", "Exp": "", "Un": false, "SA": false }, { "id_1-start": 37, "id_1-end": 43, "id_2-start": 46, "id_2-end": 49, "relation-type": "part-of", "Exp": "", "Un": false, "SA": false }, { "id_1-start": 46, "id_1-end": 49, "id_2-start": 51, "id_2-end": 51, "relation-type": "physical", "Exp": "", "Un": false, "SA": false }, { "id_1-start": 58, "id_1-end": 59, "id_2-start": 37, "id_2-end": 43, "relation-type": "part-of", "Exp": "", "Un": false, "SA": false } ]

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

More Information Needed

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, a string feature.
  • sentence: the list of tokens of this sentence, obtained with spaCy, a list of string features.
  • ner: the list of named entities in this sentence, a list of dict features.
    • id-start: the start index of the entity, a int feature.
    • id-end: the end index of the entity, a int feature.
    • entity-type: the type of the entity, a string feature.
  • relations: the list of relations in this sentence, a list of dict features.
    • id_1-start: the start index of the first entity, a int feature.
    • id_1-end: the end index of the first entity, a int feature.
    • id_2-start: the start index of the second entity, a int feature.
    • id_2-end: the end index of the second entity, a int feature.
    • relation-type: the type of the relation, a string feature.
    • Exp: the explanation of the relation type assigned, a string feature.
    • Un: uncertainty of the annotator, a bool feature.
    • SA: existence of syntax ambiguity which poses a challenge for the annotator, a bool 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

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

@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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