fix readme
Browse files- README.md +8 -381
- fewshot_link_prediction.py → nell.py +19 -16
- stats.py +6 -8
README.md
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@@ -7,10 +7,10 @@ multilinguality:
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- monolingual
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size_categories:
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- n<1K
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pretty_name:
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---
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# Dataset Card for "relbert/
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## Dataset Description
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- **Repository:** [https://github.com/xwhan/One-shot-Relational-Learning](https://github.com/xwhan/One-shot-Relational-Learning)
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- **Paper:** [https://aclanthology.org/D18-1223/](https://aclanthology.org/D18-1223/)
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@@ -20,19 +20,15 @@ pretty_name: link_prediction_nell_one
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This is NELL-ONE dataset for the few-shots link prediction proposed in [https://aclanthology.org/D18-1223/](https://aclanthology.org/D18-1223/).
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Please see [NELL paper](https://www.cs.cmu.edu/~tom/pubs/NELL_aaai15.pdf) to know more about the original dataset.
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***TODO***
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- map the wikidata id to the name of the enity for the wiki-one dataset
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## Dataset Structure
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### Data Instances
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An example of `test`
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```
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{
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"relation": "concept:sportsgamesport",
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@@ -41,377 +37,8 @@ An example of `test` of `nell` looks as follows.
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}
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```
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## Statistics on the NELL test split
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- Vocab
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-
| entity_type | nell | nell_filter | sample |
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| 49 |
-
|:-------------------------|-------:|--------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| 50 |
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| person | 4026 | 4026 | Greg, Sol Hoffman, Arthur Penn |
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| 51 |
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| book | 3835 | 3835 | The Lover, Cosmos, Vixen |
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| 52 |
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| city | 3020 | 3020 | Butte, Loma Linda, Hamilton |
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| 53 |
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| athlete | 2965 | 2965 | Reggie Bush, Juan Nicasio, Jeff Fiorentino |
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| 54 |
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| company | 2520 | 2520 | New York Times Business, Wane, Navini Networks |
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| 55 |
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| sportsteam | 1799 | 1799 | Colts, Uc Irvine Anteaters, Arkansas Tech Wonder Boys |
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| 56 |
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| clothing | 1589 | 0 | umbrellas, simple dress, matters |
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| 57 |
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| writer | 1345 | 1345 | Jenna Blum, Kelley Armstrong, Vs Naipaul |
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| 58 |
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| academicfield | 1154 | 0 | international development, wildlife management, lubbock |
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| 59 |
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| drug | 1030 | 1030 | Dulcolax, Lyrica, Metronidazol |
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| 60 |
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| televisionstation | 1018 | 1018 | Wupw Tv, Msnbc, Daily Herald |
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| 61 |
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| geopoliticallocation | 1016 | 1016 | Spicewood, Massillon, Northwestern Mexico |
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| 62 |
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| personus | 996 | 996 | James Brolin, Stephen Gyllenhaal, Kelly Preston |
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| 63 |
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| weapon | 932 | 0 | sized gas, wmd, mirv |
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| 64 |
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| coach | 924 | 924 | Gary Pinkel, Clinton White, Mike Gundy |
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| 65 |
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| journalist | 875 | 875 | Alan Cooperman, Elizabeth Kolbert, Rusty Anderson |
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| 66 |
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| room | 848 | 0 | galleried bedroom, style kitchen, bigger room |
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| 67 |
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| musicartist | 814 | 814 | Tomorrow, Threats, Circle Jerks |
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| 68 |
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| politicianus | 713 | 713 | Democrats Barack Obama, Military Personnel, Frederick Douglass |
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| 69 |
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| stadiumoreventvenue | 648 | 648 | Rebook Stadium, Balcones Heights, Boone Pickens Stadium |
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| 70 |
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| officebuildingroom | 636 | 0 | oversized guest rooms, 2 cabins, quadruple rooms |
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| 71 |
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| chemical | 620 | 0 | global warming pollution, lactose, floor |
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| 72 |
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| movie | 620 | 620 | Goldeneye, The Ten Commandments, Southland Tales |
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| 73 |
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| musician | 619 | 619 | Jeff Tweedy, Chuck Berry, Wayne Coyne |
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| 74 |
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| ceo | 619 | 619 | Hector Ruiz, Use All Rights, Michael J Critelli |
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| 75 |
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| university | 616 | 616 | Victoria College, Raytheon, Longwood |
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| 76 |
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| geopoliticalorganization | 612 | 612 | Devonshire, Clewiston, Kumasi |
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| 77 |
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| stateorprovince | 600 | 600 | Karnataka, Styria, St George |
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| 78 |
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| profession | 592 | 0 | mediators, agents, gastroenterologists |
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| 79 |
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| plant | 568 | 0 | china grass, tree ferns, deutzia |
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| 80 |
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| aquarium | 535 | 0 | 5 gallon saltwater aquarium, aquascape all glass aquarium, bor s djurpark |
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| 81 |
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| website | 533 | 533 | New Republic Magazine, Canadian, Lycos |
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| 82 |
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| county | 522 | 522 | Janesville, Denton City, Pocahontas |
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| 83 |
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| disease | 510 | 510 | Sunburn, Mental Health Issues, Behavioural Problems |
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| 84 |
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| male | 484 | 484 | Offense, Pilsen, Brisbane |
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| 85 |
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| radiostation | 476 | 476 | Job, Klrt, Geneva |
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| 86 |
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| country | 472 | 472 | Denmark, South West Asia, Latvia |
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| 87 |
-
| jobposition | 458 | 0 | construction manager, governments, senior minister |
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| 88 |
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| agriculturalproduct | 449 | 0 | acai berry, root vegetables, vehicles |
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| 89 |
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| biotechcompany | 435 | 435 | Ortho Mcneil, Cameco, Chubb Corp |
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| 90 |
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| bakedgood | 430 | 0 | shakes, 2 cookies, biscuits |
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| 91 |
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| bank | 430 | 430 | Ual, Ing Direct, State Bank |
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| 92 |
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| hotel | 428 | 428 | Shangri La, Novotel, Algonquin Hotel |
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| 93 |
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| organization | 424 | 424 | Appletv, Sports Service, Year The Red Sox |
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| 94 |
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| actor | 418 | 418 | Financial, Posts, Marilyn Monroe |
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| 95 |
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| governmentorganization | 409 | 409 | High Government, US Federal Office, Immunization Branch Texas Department |
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| 96 |
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| personeurope | 402 | 402 | Arthur Miller, Line, Steven Greenhouse |
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| 97 |
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| food | 393 | 0 | feta, red fruits, chunkier foods |
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| 98 |
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| female | 390 | 390 | Paris Hilton, Balius, Terri Seymour |
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| 99 |
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| visualizablething | 386 | 386 | Kennebunk, Cruise, Marlton |
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| 100 |
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| personcanada | 365 | 365 | Matthew Perry, Sheryl Crow, Billy Gilman |
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| 101 |
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| newspaper | 357 | 357 | Evening Times, Art New England, Morning Herald |
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| 102 |
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| visualizablescene | 357 | 357 | Vallejo, River Edge, Wagrain |
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| 103 |
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| river | 344 | 344 | Wisconsin River, Rivanna, Mississippi River |
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| 104 |
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| politician | 342 | 342 | Andrew Miller, Senator Ted Kennedy, Julie Mason |
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| 105 |
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| attraction | 339 | 339 | The London Eye, Shakespeare Globe, St James Palace |
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| 106 |
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| politicsblog | 335 | 335 | Austin American, BBC News, Oc Register |
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| 107 |
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| animal | 312 | 0 | pets, parrots, waterbirds |
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| 108 |
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| celebrity | 307 | 307 | Eliza Dushku, Yellowcard, Marion Cotillard |
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| blog | 306 | 0 | maya angelou, news com, news network |
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| building | 304 | 304 | Dream, Place, Warhol Museum |
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| 111 |
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| mammal | 304 | 0 | designer dog, commonplace animals, north america |
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| director | 299 | 299 | Vincent Minnelli, Stephen Gaghan, Fred Zinnemann |
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| 113 |
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| bodypart | 299 | 0 | chambers, atrial septum, dog s heart |
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| 114 |
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| scientificterm | 294 | 294 | Learning Invariances In Neural Nets, Cross Product, Sequential Minimal Optimization |
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| 115 |
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| personmexico | 274 | 274 | Bob Welch, David Garrard, Woody Johnson |
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| 116 |
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| island | 270 | 270 | Tango, Grand Island, Bay Of Plenty |
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| 117 |
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| personaustralia | 263 | 263 | Charles De Lint, Ford Madox Ford, Malcolm Lowry |
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| 118 |
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| transportation | 258 | 258 | Maglev, Lirr, Text |
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| 119 |
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| hobby | 258 | 0 | day trips, recreational opportunities, discussions |
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| 120 |
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| musicalbum | 257 | 257 | Two Days, Halo, Oceania |
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| 121 |
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| park | 247 | 247 | Shepherds Bush, Brockton, Sites |
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| 122 |
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| criminal | 243 | 243 | Blog Entries, Articles The 3, Lary Hoover |
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| 123 |
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| visualartist | 239 | 239 | Juan Gris, Rafael, Mattia Preti |
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| 124 |
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| airport | 236 | 236 | Cyprus, Charlotte Douglas International, North Las Vegas Airport |
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| 125 |
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| professor | 235 | 235 | Ian Flemming, Christopher Hitchens, One David |
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| 126 |
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| bathroomitem | 235 | 0 | showerhead, hot tub, large soaking tub |
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| 127 |
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| personnorthamerica | 235 | 235 | Ken Belson, Michael Albert, Florida Marlins |
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| 128 |
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| emotion | 230 | 230 | Lack, Lethargy, Type |
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| 129 |
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| school | 229 | 229 | Wayne State University, Wilberforce University, North Carolina State University |
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| 130 |
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| sportsgame | 226 | 226 | 1932 World Series, Days Last Year, 1905 World Series |
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| 131 |
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| buildingfeature | 216 | 216 | Users, Points, Front Doors |
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| 132 |
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| insect | 214 | 0 | parasitic wasps, young grubs, ladybugs |
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| 133 |
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| automobilemodel | 210 | 210 | Toyota Tacoma, 300zx, Custom 500 |
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| 134 |
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| furniture | 210 | 0 | berths, ashley furniture, bedding |
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| 135 |
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| publication | 205 | 205 | Dow Jones, News Corp, Mci |
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| 136 |
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| scientist | 204 | 204 | Epsilon, Jorge Luis Borges, Balmer |
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| 137 |
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| magazine | 203 | 203 | Harpers, Real People, Afghan Civilians |
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| 138 |
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| bird | 199 | 0 | granivorous, jacanas, large bird |
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| 139 |
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| product | 194 | 194 | Toshiba, Linux, Ms Windows |
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| 140 |
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| professionalorganization | 192 | 0 | interface, asi, healthcare reform |
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| 141 |
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| musicsong | 186 | 186 | Boys, God Of Thunder, Last Time |
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| 142 |
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| personafrica | 185 | 185 | Guillermo Del Toro, Cameron Crowe, Warner Baxter |
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| 143 |
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| automobilemaker | 177 | 177 | German Automakers, Yamaha, Gma |
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| 144 |
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| televisionshow | 176 | 176 | Concentration, Rambling Rose, Tribune |
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| 145 |
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| lake | 172 | 172 | Lake Bartlett, Crystal Lake, Lake Tahoe |
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| 146 |
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| museum | 168 | 168 | Home, Path, Forbes Galleries |
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| 147 |
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| artery | 167 | 167 | Arms, Hepatic Artery, Left Side |
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| 148 |
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| bacteria | 165 | 165 | P Falciparum, Parasite Trypanosoma Cruzi, Actinomyces Israelii |
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| 149 |
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| beverage | 164 | 0 | high efficiency water, breakfast, duet |
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| 150 |
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| language | 162 | 162 | Alemannic, Albanian, Castilian |
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| 151 |
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| comedian | 159 | 159 | Kate Douglas Wiggin, Jenny Mollen, Jean Jacques Annaud |
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| 152 |
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| politicsissue | 152 | 0 | nursing degree, resource services, journalism degree |
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| 153 |
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| mountain | 150 | 150 | Sierra Nevada, Geography, Southern Appalachian |
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| 154 |
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| landscapefeatures | 148 | 0 | deserts, sanctuary, managment |
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| 155 |
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| fish | 146 | 0 | starfish, sea bream, sea anemones |
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| 156 |
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| sportsequipment | 138 | 138 | Golf Shoes, Top, Bat |
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| 157 |
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| retailstore | 138 | 138 | Express Post, L L Bean Outdoor Discovery Schools, Stater Bros |
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| 158 |
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| musicinstrument | 135 | 135 | Acoustic Bass, Firo B, Drum |
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| 159 |
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| skiarea | 132 | 132 | Lake Erie, Fraser River, Oak Forest |
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| 160 |
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| programminglanguage | 131 | 131 | Credits, The New, Tips |
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| 161 |
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| software | 130 | 130 | System Works, Windows Server 2, Perfect Fit |
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| 162 |
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| winery | 126 | 126 | Windsor, Melville, Sanford |
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| 163 |
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| geometricshape | 125 | 125 | Theory, Degree, Regions |
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| 164 |
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| sport | 123 | 0 | area agencies, golf, professional football |
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| 165 |
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| location | 122 | 0 | convenient place, saipan, cosmopolitan areas |
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| 166 |
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| recordlabel | 121 | 121 | Cheap Trick, Columbia Records, Young Money Entertainment |
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| 167 |
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| physiologicalcondition | 121 | 0 | stomach ulcers, stress, medical illness |
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| 168 |
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| vertebrate | 120 | 0 | cattles, mountain lions, nature horses |
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| 169 |
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| physicalaction | 115 | 115 | Pakistan, Markets, Beaverton |
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| 170 |
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| personasia | 115 | 115 | Subpages, Family Members, Chat |
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| 171 |
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| musicgenre | 107 | 107 | Synth Pop, Dancehall, New Wave |
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| 172 |
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| vehicle | 103 | 0 | escape hybrid, countries, emergency vehicles |
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| 173 |
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| sportsteamposition | 102 | 102 | Offensive Lineman, Defender, Change |
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| 174 |
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| date | 101 | 0 | 1968, 1927, the end of the year |
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| 175 |
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| eventoutcome | 100 | 100 | Incredible Experience, Huge Impact, Corruption |
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| 176 |
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| vegetable | 99 | 0 | onion, radish, steamed vegetables |
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| 177 |
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| sportsleague | 98 | 98 | National Hockey League, Minors, Archives |
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| 178 |
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| arthropod | 96 | 0 | small invertebrates, woodlice, arthropods |
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| 179 |
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| politicalparty | 95 | 95 | Country Party, Gaming, House Water |
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| 180 |
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| politicaloffice | 95 | 95 | Secretary Of State, Senate President, Business Centers |
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| 181 |
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| awardtrophytournament | 95 | 95 | Super Bowl Title, Alan I Rothenberg Trophy, Medals |
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| 182 |
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| hallwayitem | 93 | 0 | panel, vent, free |
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| 183 |
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| model | 92 | 92 | Sarah Ban Breathnach, Jonathan Hunt, Ingrid |
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| 184 |
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| shoppingmall | 91 | 91 | Colonial Park, Caesars Palace, Manhattan Mall Shopping Center |
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| 185 |
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| terroristorganization | 87 | 87 | Palestinian Militants, Times, Incident |
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| 186 |
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| hospital | 86 | 86 | Plymouth Village, Prudential Center, Trinity Mother Frances |
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| 187 |
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| economicsector | 86 | 0 | search, loans insurance, insurance companies |
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| 188 |
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| braintissue | 86 | 86 | Temporal Lobe, 2, Brain Areas |
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| 189 |
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| color | 84 | 84 | Blend, Blue Pearl, Inspection |
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| 190 |
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| weatherphenomenon | 83 | 83 | Entire, Whirlwind, Thing |
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| 191 |
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| bedroomitem | 82 | 0 | order, full bath, marketplace |
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| 192 |
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| reptile | 81 | 0 | leopard, arrow, tree |
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| 193 |
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| chef | 80 | 80 | Jerome Jesus, Philippa Gregory, James Owen Smith |
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| 194 |
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| currency | 79 | 79 | Pesetas, Acres, Kwacha |
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| 195 |
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| invertebrate | 79 | 0 | mangora genus spider, wood storks, mammals |
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| 196 |
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| amphibian | 78 | 0 | crowned eagle, behavior, ferruginous hawk |
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| 197 |
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| monument | 77 | 77 | National Cathedral, Slidell, Los Alamos |
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| 198 |
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| charactertrait | 77 | 77 | Initiative, Prevention, World |
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| 199 |
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| street | 76 | 76 | Abercorn Street, Temple Street, College Street |
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| 200 |
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| visualizableobject | 72 | 72 | Jersey, Desktop Computer, Kickstand |
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| 201 |
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| restaurant | 69 | 69 | Fix, Peking Duck House Restaurant, Rite Aid Corporation |
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| 202 |
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| nongovorganization | 69 | 69 | U S, Sri Lankan Government, Lehi |
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| 203 |
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| wine | 69 | 0 | grains, johannisberg riesling, joint venture |
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| 204 |
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| buildingmaterial | 68 | 68 | Fiberglass, Stainless Steel, Filigree |
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| 205 |
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| videogame | 66 | 66 | Nights, Legend Of Zelda, Guitar Hero |
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| 206 |
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| nerve | 65 | 65 | Musculoskeletal System, Elbow, Human Brain |
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| 207 |
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| dateliteral | 65 | 0 | 1947, 1931, april 24 2013 |
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| 208 |
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| architect | 65 | 0 | albert, renzo piano, hart wood |
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| 209 |
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| monarch | 59 | 59 | Julian The Apostate, Single Mother, All Star |
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| 210 |
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| religion | 57 | 57 | Tabernacle, A Di Da Buddhist Temple, Thousand Years |
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| 211 |
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| videogamesystem | 56 | 56 | System Upgrades, Play Station, Os |
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| 212 |
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| condiment | 56 | 0 | red cabbage, bbq sauce, glaze |
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| 213 |
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| trainstation | 54 | 54 | Baltimore And Potomac Railroad Station, Allack Station, Ideal Base |
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| 214 |
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| mldataset | 54 | 54 | 38, 7, Senior |
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| 215 |
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| year | 53 | 53 | 1887, 1881, 1903 |
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| 216 |
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| personsouthamerica | 52 | 52 | Pepita Jimenez, Juan Valera, Michael Grant |
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| 217 |
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| kitchenitem | 52 | 0 | large refrigerator, staple, double oven |
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| 218 |
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| beach | 52 | 52 | Brandeis, Supply, W Palm Beach |
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| 219 |
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| astronaut | 51 | 51 | Discovery, Vance Brand, Charles Stross |
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| 220 |
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| crimeorcharge | 50 | 0 | credit, probation, violent crimes |
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| 221 |
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| parlourgame | 50 | 50 | The Man With The Twisted Lip, Six Hour Drive, Water |
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| 222 |
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| ethnicgroup | 50 | 50 | Soviet, Darling, Same Way |
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| 223 |
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| muscle | 50 | 50 | Hamstring, Transverse, Abdominal Muscles |
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| 224 |
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| farm | 49 | 49 | The United States, Puddicombe Farms, Orchard Valley Farms |
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| 225 |
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| nonprofitorganization | 49 | 49 | Haven, Humane Society, Drools |
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| 226 |
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| zoo | 49 | 49 | Denver Zoo, Saint Louis Zoo, Port Clinton |
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| 227 |
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| agent | 47 | 0 | anderson, derek powazek, liberty media corp |
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| 228 |
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| consumerelectronicitem | 47 | 47 | Ds Lite, Gba Sp, Ratings |
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| 229 |
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| tool | 47 | 47 | Checklists, Tools, Laundry Machine |
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| 230 |
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| port | 46 | 46 | Tampa International Airport, Kalmar, Ronald Reagan National Airport |
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| 231 |
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| mlauthor | 46 | 46 | Web Search, Alexander Doni, Yoram Singer |
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| 232 |
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| meat | 46 | 0 | aunt, flank steak, cream sauce |
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| 233 |
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| mollusk | 46 | 0 | rays, sea shells, find |
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| 234 |
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| crustacean | 45 | 0 | large image, lvmh, open directory project |
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| 235 |
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| musicfestival | 45 | 45 | Yamaha Music Festival, Wygant State Natural Area, William M Tugman State Park |
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| 236 |
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| tableitem | 43 | 0 | amr, registry, favor |
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| 237 |
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| planet | 42 | 42 | Result, Potpourri, Matter |
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| 238 |
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| conference | 41 | 41 | Bs, Uae, Strc |
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| 239 |
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| tradeunion | 40 | 0 | gmp, operators, legend |
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| 240 |
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| skyscraper | 40 | 40 | Compromise, Cn Tower, International City |
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| 241 |
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| protein | 40 | 40 | Insulin, Collagen, Laboratory |
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| 242 |
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| arachnid | 40 | 0 | movies, meetings, tick |
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| 243 |
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| nondiseasecondition | 39 | 39 | Job, System, Production |
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| 244 |
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| visualartform | 36 | 36 | Famous Painting, Perfume, Sculpture |
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| 245 |
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| grain | 35 | 0 | popcorn, jasmine rice, pole beans |
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| 246 |
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| visualartmovement | 34 | 34 | Surrealism, Dada, Britain |
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| 247 |
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| fruit | 34 | 0 | oranges, peaches, black berries |
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| 248 |
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| wallitem | 33 | 33 | Writing, Times, Transfer |
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| 249 |
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| candy | 33 | 0 | heart, candy corn, bounty |
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| 250 |
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| bone | 32 | 0 | two, human bone marrow, one arm |
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| 251 |
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| election | 30 | 30 | Same Name, Charles De Gaulle, Death |
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| 252 |
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| mlsoftware | 30 | 0 | vlc, spies, instance |
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| 253 |
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| highway | 30 | 30 | Four Miles, West Branch, First Place |
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| 254 |
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| event | 29 | 29 | Obtain, Tsunami Relief, Liquidity Crisis |
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| 255 |
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| placeofworship | 28 | 28 | Htilominlo Temple, Fort George, Wallington |
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| 256 |
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| nonneginteger | 28 | 28 | 90, 2, 4 |
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| 257 |
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| visualizableattribute | 27 | 27 | Dots, Huge, Cloud |
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| 258 |
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| boardgame | 27 | 27 | Winners, Truck, Risk |
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| 259 |
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| politicsbill | 26 | 26 | Climate Stewardship Acts, Patriot Act, Courtesy |
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| 260 |
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| householditem | 26 | 26 | Home Heating, Size Sofabed, Range |
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| 261 |
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| creditunion | 25 | 25 | Banks, Bad Credit Mortgage Loan, First State |
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| 262 |
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| perceptionaction | 25 | 25 | Iowa City, Committee Hearing, Ames |
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| 263 |
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| automobileengine | 24 | 24 | Ford, Publishing, Ford Escort |
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| 264 |
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| creativework | 24 | 24 | Bad Dreams, Bedtime Stories, Evil Dead |
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| 265 |
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| mlarea | 22 | 22 | Roles, Conditional Random Fields, Graphical Models |
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| 266 |
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| nut | 22 | 0 | year s, sesame seeds, living |
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| 267 |
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| race | 22 | 22 | Queensland, Morbihan, July 2 |
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| 268 |
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| personalcareitem | 22 | 22 | Tent, Wash, Brush |
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| 269 |
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| mlalgorithm | 22 | 22 | Design, 2 0, Sons |
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| 270 |
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| vein | 21 | 21 | Forage, Viscera, Adipose |
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| 271 |
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| sociopolitical | 21 | 21 | Output, Development, Government |
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| 272 |
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| researchproject | 21 | 21 | Science, Feasibility Study, Education Research |
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| 273 |
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| bridge | 20 | 20 | High Bridge, Honor, Millennium Bridge |
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| 274 |
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| archaea | 20 | 20 | Sulfolobus Acidocaldarius, Halobacteria, A |
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| 275 |
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| cheese | 19 | 19 | Jack Cheese, Ricotta Cheese, Fontina Cheese |
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| 276 |
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| legume | 19 | 0 | washing machine, string beans, southwestern |
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| 277 |
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| mountainrange | 17 | 17 | Appalachian, West Hills, Eagle Creek |
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| 278 |
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| physicalcharacteristic | 17 | 17 | Humanity, Strictest, Foray |
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| 279 |
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| cardgame | 16 | 16 | Taking, UK, Free |
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| 280 |
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| medicalprocedure | 16 | 16 | Cpr, Chamber, Oxygen |
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| 281 |
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| sportsevent | 16 | 16 | Short Months, Decade, Please Contact |
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| 282 |
-
| militaryconflict | 15 | 15 | Six Day War, Great War, War |
|
| 283 |
-
| mlmetric | 15 | 15 | 9 11, Sums, Variation |
|
| 284 |
-
| petroleumrefiningcompany | 14 | 14 | Valero, Bp America, Ultramar Diamond Shamrock |
|
| 285 |
-
| celltype | 14 | 14 | Liver, Women S Hospital, Capacity |
|
| 286 |
-
| traditionalgame | 13 | 13 | Horseshoe Casino, 3 5, Daughters |
|
| 287 |
-
| mlconference | 13 | 13 | Interview, Kingman, Services The Ability |
|
| 288 |
-
| convention | 12 | 12 | Conversation, Technorati, Condition |
|
| 289 |
-
| meetingeventtitle | 12 | 12 | Brain Maps To Mechanisms Neural Circuit Molecular Architecture, Robot Futures, Math Monkeys And The Developing Brain |
|
| 290 |
-
| trail | 11 | 11 | In New Jersey, Miller Park, In Philadelphia |
|
| 291 |
-
| meetingeventtype | 11 | 11 | Joint Department Of Neurobiology And Center For The Neural Basis Of Cognition And Systems Neuroscience Institue Seminar, Innovators Forum, Scs Author Presentation Booksigning |
|
| 292 |
-
| judge | 11 | 11 | Ny, Times Union, Parsons |
|
| 293 |
-
| physicsterm | 10 | 10 | Momentum, 19 9 Percent, 15 Miles |
|
| 294 |
-
| coffeedrink | 10 | 0 | coffee, mocha, turkish coffee |
|
| 295 |
-
| cognitiveactions | 10 | 10 | Goal, Abilities, Learning |
|
| 296 |
-
| zipcode | 10 | 10 | 19103, 33130, 77063 |
|
| 297 |
-
| mediatype | 10 | 10 | Databases, Irrigation, Medium |
|
| 298 |
-
| month | 10 | 0 | december, november, march |
|
| 299 |
-
| perceptionevent | 9 | 9 | Beep, Macromedia Flash, Light |
|
| 300 |
-
| cave | 8 | 0 | san gabriel, will, ie |
|
| 301 |
-
| lymphnode | 8 | 8 | Healthy Eating, Lymph Nodes, Lymph System |
|
| 302 |
-
| continent | 7 | 7 | European Continent, South America, Europe |
|
| 303 |
-
| filmfestival | 7 | 7 | Beginning, Igoogle, New York Times Company |
|
| 304 |
-
| officeitem | 7 | 7 | Capacities, Policy Page, Credit Card Click |
|
| 305 |
-
| fungus | 7 | 0 | compound, cook, enrichment |
|
| 306 |
-
| url | 6 | 6 | Direct Links, Numerous Links, Links Links |
|
| 307 |
-
| olympics | 6 | 6 | CBS Television Network, Palestinian National Authority, Mayor |
|
| 308 |
-
| game | 6 | 6 | Aspects, Designee The Vice, Nice 3 |
|
| 309 |
-
| flooritem | 5 | 5 | Storage Tank, Waste Management, Water Quality |
|
| 310 |
-
| televisionnetwork | 5 | 5 | Cw, PBS, Independent |
|
| 311 |
-
| time | 4 | 4 | 8 30 P M, 7 Click, 4 00 P M Contact |
|
| 312 |
-
| mediacompany | 4 | 4 | Vibrant Media, Media Public, Simplify Media |
|
| 313 |
-
| dayofweek | 4 | 4 | Monday, Wednesday, Tuesday |
|
| 314 |
-
| caf_ | 3 | 3 | Starbucks Coffee Company, Sbux, Tim Hortons |
|
| 315 |
-
| victim | 3 | 3 | Resources Students, Health Sciences Students, Technology Students |
|
| 316 |
-
| militaryeventtype | 3 | 3 | Regimes, Attacks, Clashes |
|
| 317 |
-
| item | 3 | 3 | Laundry Detergent, Laptop, Dishes |
|
| 318 |
-
| geolocatablething | 3 | 3 | Buildings, Iquique, Cars |
|
| 319 |
-
| politicsgroup | 2 | 2 | Description, Moveon Org |
|
| 320 |
-
| gamescore | 2 | 2 | 4 5, 2 3 |
|
| 321 |
-
| refineryproduct | 2 | 2 | Coolant, East Coast Ports |
|
| 322 |
-
| grandprix | 2 | 2 | Renault, Bernie Ecclestone |
|
| 323 |
-
| personbylocation | 1 | 1 | Dan Garton |
|
| 324 |
-
| species | 1 | 1 | Marine Flora |
|
| 325 |
-
| humanagent | 1 | 1 | Monsanto Co S G D Searle Division |
|
| 326 |
-
| virus | 1 | 1 | Safe Drinking Water |
|
| 327 |
-
| recipe | 1 | 1 | Rice |
|
| 328 |
-
| SUM | 68518 | 53887 | |
|
| 329 |
-
|
| 330 |
-
- Head/Tail Entity Distribution
|
| 331 |
-
|
| 332 |
-
| entity_type | nell (head) | nell_filter (head) | nell (tail) | nell_filter (tail) |
|
| 333 |
-
|:-------------------------|--------------:|---------------------:|--------------:|---------------------:|
|
| 334 |
-
| politicianus | 352 | 352 | 360 | 360 |
|
| 335 |
-
| sportsteam | 295 | 295 | 0 | 0 |
|
| 336 |
-
| automobilemaker | 274 | 273 | 54 | 54 |
|
| 337 |
-
| insect | 230 | 0 | 270 | 0 |
|
| 338 |
-
| automobilemodel | 100 | 100 | 0 | 0 |
|
| 339 |
-
| animal | 97 | 0 | 30 | 0 |
|
| 340 |
-
| sport | 93 | 0 | 74 | 0 |
|
| 341 |
-
| agriculturalproduct | 87 | 0 | 0 | 0 |
|
| 342 |
-
| sportsgame | 74 | 0 | 0 | 0 |
|
| 343 |
-
| product | 62 | 62 | 0 | 0 |
|
| 344 |
-
| software | 42 | 42 | 0 | 0 |
|
| 345 |
-
| city | 42 | 42 | 161 | 161 |
|
| 346 |
-
| stateorprovince | 38 | 38 | 0 | 0 |
|
| 347 |
-
| athlete | 34 | 0 | 59 | 59 |
|
| 348 |
-
| organization | 32 | 32 | 2 | 2 |
|
| 349 |
-
| arthropod | 32 | 0 | 41 | 0 |
|
| 350 |
-
| beverage | 27 | 0 | 0 | 0 |
|
| 351 |
-
| country | 27 | 27 | 317 | 91 |
|
| 352 |
-
| geopoliticallocation | 24 | 24 | 14 | 8 |
|
| 353 |
-
| mammal | 23 | 0 | 0 | 0 |
|
| 354 |
-
| politician | 23 | 23 | 58 | 58 |
|
| 355 |
-
| person | 14 | 0 | 96 | 96 |
|
| 356 |
-
| invertebrate | 14 | 0 | 43 | 0 |
|
| 357 |
-
| personmexico | 13 | 0 | 20 | 20 |
|
| 358 |
-
| crustacean | 11 | 0 | 25 | 0 |
|
| 359 |
-
| coffeedrink | 11 | 0 | 0 | 0 |
|
| 360 |
-
| school | 11 | 11 | 0 | 0 |
|
| 361 |
-
| hobby | 10 | 0 | 0 | 0 |
|
| 362 |
-
| county | 10 | 10 | 4 | 4 |
|
| 363 |
-
| vegetable | 8 | 0 | 0 | 0 |
|
| 364 |
-
| reptile | 4 | 0 | 0 | 0 |
|
| 365 |
-
| personaustralia | 4 | 0 | 5 | 5 |
|
| 366 |
-
| videogame | 4 | 4 | 0 | 0 |
|
| 367 |
-
| food | 4 | 0 | 0 | 0 |
|
| 368 |
-
| celebrity | 4 | 4 | 2 | 2 |
|
| 369 |
-
| male | 3 | 1 | 5 | 5 |
|
| 370 |
-
| female | 3 | 3 | 3 | 3 |
|
| 371 |
-
| visualizablescene | 3 | 3 | 3 | 3 |
|
| 372 |
-
| grain | 2 | 0 | 0 | 0 |
|
| 373 |
-
| vehicle | 2 | 0 | 0 | 0 |
|
| 374 |
-
| amphibian | 2 | 0 | 0 | 0 |
|
| 375 |
-
| legume | 1 | 0 | 0 | 0 |
|
| 376 |
-
| company | 1 | 1 | 147 | 144 |
|
| 377 |
-
| arachnid | 1 | 0 | 6 | 0 |
|
| 378 |
-
| coach | 1 | 0 | 245 | 245 |
|
| 379 |
-
| mlsoftware | 1 | 0 | 0 | 0 |
|
| 380 |
-
| fruit | 1 | 0 | 0 | 0 |
|
| 381 |
-
| professor | 1 | 1 | 0 | 0 |
|
| 382 |
-
| island | 1 | 1 | 0 | 0 |
|
| 383 |
-
| chemical | 1 | 0 | 0 | 0 |
|
| 384 |
-
| personus | 1 | 1 | 6 | 6 |
|
| 385 |
-
| personnorthamerica | 1 | 0 | 3 | 3 |
|
| 386 |
-
| geopoliticalorganization | 1 | 1 | 17 | 7 |
|
| 387 |
-
| drug | 1 | 1 | 0 | 0 |
|
| 388 |
-
| planet | 0 | 0 | 1 | 1 |
|
| 389 |
-
| bodypart | 0 | 0 | 69 | 0 |
|
| 390 |
-
| criminal | 0 | 0 | 1 | 1 |
|
| 391 |
-
| director | 0 | 0 | 1 | 1 |
|
| 392 |
-
| personeurope | 0 | 0 | 1 | 1 |
|
| 393 |
-
| astronaut | 0 | 0 | 1 | 1 |
|
| 394 |
-
| journalist | 0 | 0 | 1 | 1 |
|
| 395 |
-
| location | 0 | 0 | 2 | 0 |
|
| 396 |
-
| biotechcompany | 0 | 0 | 11 | 10 |
|
| 397 |
-
|
| 398 |
-
- Relation Size
|
| 399 |
-
|
| 400 |
-
| relation_type | nell | nell_filter |
|
| 401 |
-
|:-----------------------------------------------|-------:|--------------:|
|
| 402 |
-
| concept:animalsuchasinvertebrate | 415 | 0 |
|
| 403 |
-
| concept:politicianusendorsespoliticianus | 386 | 386 |
|
| 404 |
-
| concept:teamcoach | 341 | 341 |
|
| 405 |
-
| concept:producedby | 213 | 209 |
|
| 406 |
-
| concept:automobilemakerdealersincity | 178 | 177 |
|
| 407 |
-
| concept:geopoliticallocationresidenceofpersion | 143 | 143 |
|
| 408 |
-
| concept:agriculturalproductcamefromcountry | 140 | 0 |
|
| 409 |
-
| concept:sportschoolincountry | 103 | 0 |
|
| 410 |
-
| concept:automobilemakerdealersincountry | 96 | 96 |
|
| 411 |
-
| concept:sportsgamesport | 74 | 0 |
|
| 412 |
-
| concept:athleteinjuredhisbodypart | 69 | 0 |
|
| 413 |
|
| 414 |
-
|
| 415 |
```
|
| 416 |
@inproceedings{xiong-etal-2018-one,
|
| 417 |
title = "One-Shot Relational Learning for Knowledge Graphs",
|
|
|
|
| 7 |
- monolingual
|
| 8 |
size_categories:
|
| 9 |
- n<1K
|
| 10 |
+
pretty_name: nell
|
| 11 |
---
|
| 12 |
|
| 13 |
+
# Dataset Card for "relbert/nell"
|
| 14 |
## Dataset Description
|
| 15 |
- **Repository:** [https://github.com/xwhan/One-shot-Relational-Learning](https://github.com/xwhan/One-shot-Relational-Learning)
|
| 16 |
- **Paper:** [https://aclanthology.org/D18-1223/](https://aclanthology.org/D18-1223/)
|
|
|
|
| 20 |
This is NELL-ONE dataset for the few-shots link prediction proposed in [https://aclanthology.org/D18-1223/](https://aclanthology.org/D18-1223/).
|
| 21 |
Please see [NELL paper](https://www.cs.cmu.edu/~tom/pubs/NELL_aaai15.pdf) to know more about the original dataset.
|
| 22 |
|
| 23 |
+
| train | validation | test |
|
| 24 |
+
|------:|-----------:|-----:|
|
| 25 |
+
|8,526 | 1,004 | 2,158 |
|
| 26 |
+
|5,498 | 878 | 1,352 |
|
| 27 |
|
| 28 |
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|
| 29 |
## Dataset Structure
|
| 30 |
### Data Instances
|
| 31 |
+
An example of `test` looks as follows.
|
| 32 |
```
|
| 33 |
{
|
| 34 |
"relation": "concept:sportsgamesport",
|
|
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|
| 37 |
}
|
| 38 |
```
|
| 39 |
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|
| 40 |
|
| 41 |
+
## Citation Information
|
| 42 |
```
|
| 43 |
@inproceedings{xiong-etal-2018-one,
|
| 44 |
title = "One-Shot Relational Learning for Knowledge Graphs",
|
fewshot_link_prediction.py → nell.py
RENAMED
|
@@ -4,8 +4,8 @@ import datasets
|
|
| 4 |
|
| 5 |
logger = datasets.logging.get_logger(__name__)
|
| 6 |
_DESCRIPTION = """Few shots link prediction dataset. """
|
| 7 |
-
_NAME = "
|
| 8 |
-
_VERSION = "0.0.
|
| 9 |
_CITATION = """
|
| 10 |
@inproceedings{xiong-etal-2018-one,
|
| 11 |
title = "One-Shot Relational Learning for Knowledge Graphs",
|
|
@@ -29,15 +29,20 @@ _CITATION = """
|
|
| 29 |
_HOME_PAGE = "https://github.com/asahi417/relbert"
|
| 30 |
_URL = f'https://huggingface.co/datasets/relbert/{_NAME}/resolve/main/data'
|
| 31 |
# _TYPES = ["nell_filter", "nell", "wiki"]
|
| 32 |
-
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
|
| 36 |
-
|
| 37 |
-
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|
| 38 |
|
| 39 |
|
| 40 |
-
class
|
| 41 |
"""BuilderConfig"""
|
| 42 |
|
| 43 |
def __init__(self, **kwargs):
|
|
@@ -45,19 +50,17 @@ class FewShotLinkPredictionConfig(datasets.BuilderConfig):
|
|
| 45 |
Args:
|
| 46 |
**kwargs: keyword arguments forwarded to super.
|
| 47 |
"""
|
| 48 |
-
super(
|
| 49 |
|
| 50 |
|
| 51 |
-
class
|
| 52 |
"""Dataset."""
|
| 53 |
|
| 54 |
-
BUILDER_CONFIGS = [
|
| 55 |
-
FewShotLinkPredictionConfig(name=i, version=datasets.Version(_VERSION), description=_DESCRIPTION)
|
| 56 |
-
for i in sorted(_TYPES)
|
| 57 |
-
]
|
| 58 |
|
| 59 |
def _split_generators(self, dl_manager):
|
| 60 |
-
downloaded_file = dl_manager.download_and_extract(_URLS[self.config.name])
|
|
|
|
| 61 |
return [datasets.SplitGenerator(name=i, gen_kwargs={"filepaths": downloaded_file[str(i)]})
|
| 62 |
for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]
|
| 63 |
|
|
|
|
| 4 |
|
| 5 |
logger = datasets.logging.get_logger(__name__)
|
| 6 |
_DESCRIPTION = """Few shots link prediction dataset. """
|
| 7 |
+
_NAME = "nell"
|
| 8 |
+
_VERSION = "0.0.6"
|
| 9 |
_CITATION = """
|
| 10 |
@inproceedings{xiong-etal-2018-one,
|
| 11 |
title = "One-Shot Relational Learning for Knowledge Graphs",
|
|
|
|
| 29 |
_HOME_PAGE = "https://github.com/asahi417/relbert"
|
| 30 |
_URL = f'https://huggingface.co/datasets/relbert/{_NAME}/resolve/main/data'
|
| 31 |
# _TYPES = ["nell_filter", "nell", "wiki"]
|
| 32 |
+
# _URLS = {i: {
|
| 33 |
+
# str(datasets.Split.TRAIN): [f'{_URL}/{i}.train.jsonl'],
|
| 34 |
+
# str(datasets.Split.VALIDATION): [f'{_URL}/{i}.validation.jsonl'],
|
| 35 |
+
# str(datasets.Split.TEST): [f'{_URL}/{i}.test.jsonl']
|
| 36 |
+
# } for i in _TYPES}
|
| 37 |
+
_TYPE = "nell_filter"
|
| 38 |
+
_URLS = {
|
| 39 |
+
str(datasets.Split.TRAIN): [f'{_URL}/{_TYPE}.train.jsonl'],
|
| 40 |
+
str(datasets.Split.VALIDATION): [f'{_URL}/{_TYPE}.validation.jsonl'],
|
| 41 |
+
str(datasets.Split.TEST): [f'{_URL}/{_TYPE}.test.jsonl']
|
| 42 |
+
}
|
| 43 |
|
| 44 |
|
| 45 |
+
class NELLConfig(datasets.BuilderConfig):
|
| 46 |
"""BuilderConfig"""
|
| 47 |
|
| 48 |
def __init__(self, **kwargs):
|
|
|
|
| 50 |
Args:
|
| 51 |
**kwargs: keyword arguments forwarded to super.
|
| 52 |
"""
|
| 53 |
+
super(NELLConfig, self).__init__(**kwargs)
|
| 54 |
|
| 55 |
|
| 56 |
+
class NELL(datasets.GeneratorBasedBuilder):
|
| 57 |
"""Dataset."""
|
| 58 |
|
| 59 |
+
BUILDER_CONFIGS = [NELLConfig(name=_TYPE, version=datasets.Version(_VERSION), description=_DESCRIPTION)]
|
|
|
|
|
|
|
|
|
|
| 60 |
|
| 61 |
def _split_generators(self, dl_manager):
|
| 62 |
+
# downloaded_file = dl_manager.download_and_extract(_URLS[self.config.name])
|
| 63 |
+
downloaded_file = dl_manager.download_and_extract(_URLS)
|
| 64 |
return [datasets.SplitGenerator(name=i, gen_kwargs={"filepaths": downloaded_file[str(i)]})
|
| 65 |
for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]
|
| 66 |
|
stats.py
CHANGED
|
@@ -1,10 +1,9 @@
|
|
| 1 |
import pandas as pd
|
| 2 |
-
import numpy as np
|
| 3 |
from datasets import load_dataset
|
| 4 |
from itertools import chain
|
| 5 |
|
| 6 |
tmp = {}
|
| 7 |
-
for _type in ['
|
| 8 |
with open(f"data/{_type}.vocab.txt") as f:
|
| 9 |
vocab = pd.DataFrame([i.split("\t") for i in f.read().split('\n') if len(i) > 0], columns=["entity", "type"])
|
| 10 |
vocab_df = vocab.groupby("type").count().sort_values(by="entity", ascending=False)
|
|
@@ -32,8 +31,8 @@ for split in ['train', 'validation', 'test']:
|
|
| 32 |
print(split)
|
| 33 |
|
| 34 |
tmp = {}
|
| 35 |
-
for _type in ['
|
| 36 |
-
data = load_dataset("relbert/
|
| 37 |
df = data.to_pandas()
|
| 38 |
|
| 39 |
tail = df.groupby("tail_type")['relation'].count().to_dict()
|
|
@@ -56,10 +55,9 @@ for split in ['train', 'validation', 'test']:
|
|
| 56 |
print(df.to_markdown(index=False))
|
| 57 |
|
| 58 |
tmp = {}
|
| 59 |
-
|
| 60 |
-
|
| 61 |
-
|
| 62 |
-
tmp[_type] = df.groupby("relation").count()['head']
|
| 63 |
keys = set(list(chain(*[list(v.index) for v in tmp.values()])))
|
| 64 |
df = pd.DataFrame([{
|
| 65 |
"relation_type": k,
|
|
|
|
| 1 |
import pandas as pd
|
|
|
|
| 2 |
from datasets import load_dataset
|
| 3 |
from itertools import chain
|
| 4 |
|
| 5 |
tmp = {}
|
| 6 |
+
for _type in ['nell_filter']:
|
| 7 |
with open(f"data/{_type}.vocab.txt") as f:
|
| 8 |
vocab = pd.DataFrame([i.split("\t") for i in f.read().split('\n') if len(i) > 0], columns=["entity", "type"])
|
| 9 |
vocab_df = vocab.groupby("type").count().sort_values(by="entity", ascending=False)
|
|
|
|
| 31 |
print(split)
|
| 32 |
|
| 33 |
tmp = {}
|
| 34 |
+
for _type in ['nell_filter']:
|
| 35 |
+
data = load_dataset("relbert/nell", _type, split=split)
|
| 36 |
df = data.to_pandas()
|
| 37 |
|
| 38 |
tail = df.groupby("tail_type")['relation'].count().to_dict()
|
|
|
|
| 55 |
print(df.to_markdown(index=False))
|
| 56 |
|
| 57 |
tmp = {}
|
| 58 |
+
data = load_dataset("relbert/nell", split=split)
|
| 59 |
+
df = data.to_pandas()
|
| 60 |
+
tmp[_type] = df.groupby("relation").count()['head']
|
|
|
|
| 61 |
keys = set(list(chain(*[list(v.index) for v in tmp.values()])))
|
| 62 |
df = pd.DataFrame([{
|
| 63 |
"relation_type": k,
|