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nell

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asahi417 commited on
Commit
ba55817
•
1 Parent(s): eba37c8

init

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Files changed (16) hide show
  1. .gitattributes +2 -0
  2. README.md +362 -139
  3. data/nell.vocab.raw.txt +3 -0
  4. data/nell.vocab.txt +2 -2
  5. data/nell_filter.vocab.txt +2 -2
  6. data/wiki.vocab.raw.txt +3 -0
  7. generate_filtered_data.py +3 -6
  8. process.py +5 -4
  9. stats.py +64 -14
  10. stats/stats.test.entity.csv +64 -0
  11. stats/stats.test.relation.csv +12 -0
  12. stats/stats.train.entity.csv +150 -0
  13. stats/stats.train.relation.csv +52 -0
  14. stats/stats.validation.entity.csv +32 -0
  15. stats/stats.validation.relation.csv +6 -0
  16. stats/stats.vocab.csv +280 -0
.gitattributes CHANGED
@@ -68,3 +68,5 @@ data/nell_filter.test.jsonl filter=lfs diff=lfs merge=lfs -text
68
  data/nell_filter.train.jsonl filter=lfs diff=lfs merge=lfs -text
69
  data/nell_filter.validation.jsonl filter=lfs diff=lfs merge=lfs -text
70
  data/nell_filter.vocab.txt filter=lfs diff=lfs merge=lfs -text
 
 
 
68
  data/nell_filter.train.jsonl filter=lfs diff=lfs merge=lfs -text
69
  data/nell_filter.validation.jsonl filter=lfs diff=lfs merge=lfs -text
70
  data/nell_filter.vocab.txt filter=lfs diff=lfs merge=lfs -text
71
+ data/nell.vocab.raw.txt filter=lfs diff=lfs merge=lfs -text
72
+ data/wiki.vocab.raw.txt filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -42,150 +42,373 @@ An example of `test` of `nell` looks as follows.
42
 
43
  ## Statistics on the NELL test split
44
 
 
45
 
46
- - Entity Types (nell)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
47
 
48
- | entity_type | tail | head |
49
- |:-------------------------|-------:|-------:|
50
- | videogame | 0 | 4 |
51
- | crustacean | 25 | 11 |
52
- | organization | 2 | 32 |
53
- | invertebrate | 43 | 14 |
54
- | agriculturalproduct | 0 | 87 |
55
- | astronaut | 1 | 0 |
56
- | vegetable | 0 | 8 |
57
- | geopoliticallocation | 14 | 24 |
58
- | person | 96 | 14 |
59
- | drug | 0 | 1 |
60
- | arthropod | 41 | 32 |
61
- | location | 2 | 0 |
62
- | company | 147 | 1 |
63
- | female | 3 | 3 |
64
- | product | 0 | 62 |
65
- | chemical | 0 | 1 |
66
- | legume | 0 | 1 |
67
- | county | 4 | 10 |
68
- | mlsoftware | 0 | 1 |
69
- | sport | 74 | 93 |
70
- | fruit | 0 | 1 |
71
- | personeurope | 1 | 0 |
72
- | planet | 1 | 0 |
73
- | mammal | 0 | 23 |
74
- | professor | 0 | 1 |
75
- | criminal | 1 | 0 |
76
- | athlete | 59 | 34 |
77
- | insect | 270 | 230 |
78
- | school | 0 | 11 |
79
- | automobilemodel | 0 | 100 |
80
- | coffeedrink | 0 | 11 |
81
- | food | 0 | 4 |
82
- | celebrity | 2 | 4 |
83
- | biotechcompany | 11 | 0 |
84
- | animal | 30 | 97 |
85
- | visualizablescene | 3 | 3 |
86
- | politician | 58 | 23 |
87
- | software | 0 | 42 |
88
- | sportsgame | 0 | 74 |
89
- | grain | 0 | 2 |
90
- | city | 161 | 42 |
91
- | personaustralia | 5 | 4 |
92
- | politicianus | 360 | 352 |
93
- | hobby | 0 | 10 |
94
- | arachnid | 6 | 1 |
95
- | country | 317 | 27 |
96
- | male | 5 | 3 |
97
- | personnorthamerica | 3 | 1 |
98
- | sportsteam | 0 | 295 |
99
- | stateorprovince | 0 | 38 |
100
- | personus | 6 | 1 |
101
- | coach | 245 | 1 |
102
- | director | 1 | 0 |
103
- | automobilemaker | 54 | 274 |
104
- | reptile | 0 | 4 |
105
- | journalist | 1 | 0 |
106
- | vehicle | 0 | 2 |
107
- | bodypart | 69 | 0 |
108
- | amphibian | 0 | 2 |
109
- | beverage | 0 | 27 |
110
- | island | 0 | 1 |
111
- | geopoliticalorganization | 17 | 1 |
112
- | personmexico | 20 | 13 |
113
 
114
- - Relation Types (nell)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
115
 
116
- | relation | relation |
117
- |:-----------------------------------------------|-----------:|
118
- | concept:agriculturalproductcamefromcountry | 140 |
119
- | concept:animalsuchasinvertebrate | 415 |
120
- | concept:athleteinjuredhisbodypart | 69 |
121
- | concept:automobilemakerdealersincity | 178 |
122
- | concept:automobilemakerdealersincountry | 96 |
123
- | concept:geopoliticallocationresidenceofpersion | 143 |
124
- | concept:politicianusendorsespoliticianus | 386 |
125
- | concept:producedby | 213 |
126
- | concept:sportschoolincountry | 103 |
127
- | concept:sportsgamesport | 74 |
128
- | concept:teamcoach | 341 |
129
-
130
- - Vocab Size (nell): 68544
131
-
132
- - Entity Types (nell_filter)
133
-
134
- | entity_type | tail | head |
135
- |:-------------------------|-------:|-------:|
136
- | videogame | 0 | 4 |
137
- | organization | 2 | 32 |
138
- | astronaut | 1 | 0 |
139
- | geopoliticallocation | 8 | 24 |
140
- | person | 96 | 0 |
141
- | drug | 0 | 1 |
142
- | company | 144 | 1 |
143
- | female | 3 | 3 |
144
- | product | 0 | 62 |
145
- | county | 4 | 10 |
146
- | personeurope | 1 | 0 |
147
- | planet | 1 | 0 |
148
- | professor | 0 | 1 |
149
- | criminal | 1 | 0 |
150
- | athlete | 59 | 0 |
151
- | school | 0 | 11 |
152
- | automobilemodel | 0 | 100 |
153
- | celebrity | 2 | 4 |
154
- | biotechcompany | 10 | 0 |
155
- | visualizablescene | 3 | 3 |
156
- | politician | 58 | 23 |
157
- | software | 0 | 42 |
158
- | city | 161 | 42 |
159
- | personaustralia | 5 | 0 |
160
- | politicianus | 360 | 352 |
161
- | country | 91 | 27 |
162
- | male | 5 | 1 |
163
- | personnorthamerica | 3 | 0 |
164
- | sportsteam | 0 | 295 |
165
- | stateorprovince | 0 | 38 |
166
- | personus | 6 | 1 |
167
- | coach | 245 | 0 |
168
- | director | 1 | 0 |
169
- | automobilemaker | 54 | 273 |
170
- | journalist | 1 | 0 |
171
- | island | 0 | 1 |
172
- | geopoliticalorganization | 7 | 1 |
173
- | personmexico | 20 | 0 |
174
-
175
- - Relation Types (nell_filter)
176
-
177
- | relation | relation |
178
- |:-----------------------------------------------|-----------:|
179
- | concept:automobilemakerdealersincity | 177 |
180
- | concept:automobilemakerdealersincountry | 96 |
181
- | concept:geopoliticallocationresidenceofpersion | 143 |
182
- | concept:politicianusendorsespoliticianus | 386 |
183
- | concept:producedby | 209 |
184
- | concept:teamcoach | 341 |
185
-
186
- Vocab Size (nell_filter)
187
- 53887
188
 
 
 
 
 
 
 
 
 
 
 
 
 
 
189
 
190
  ### Citation Information
191
  ```
 
42
 
43
  ## Statistics on the NELL test split
44
 
45
+ - Vocab
46
 
47
+ | entity_type | nell | nell_filter | sample |
48
+ |:-------------------------|-------:|--------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
49
+ | person | 4026 | 4026 | Greg, Sol Hoffman, Arthur Penn |
50
+ | book | 3835 | 3835 | The Lover, Cosmos, Vixen |
51
+ | city | 3020 | 3020 | Butte, Loma Linda, Hamilton |
52
+ | athlete | 2965 | 2965 | Reggie Bush, Juan Nicasio, Jeff Fiorentino |
53
+ | company | 2520 | 2520 | New York Times Business, Wane, Navini Networks |
54
+ | sportsteam | 1799 | 1799 | Colts, Uc Irvine Anteaters, Arkansas Tech Wonder Boys |
55
+ | clothing | 1589 | 0 | umbrellas, simple dress, matters |
56
+ | writer | 1345 | 1345 | Jenna Blum, Kelley Armstrong, Vs Naipaul |
57
+ | academicfield | 1154 | 0 | international development, wildlife management, lubbock |
58
+ | drug | 1030 | 1030 | Dulcolax, Lyrica, Metronidazol |
59
+ | televisionstation | 1018 | 1018 | Wupw Tv, Msnbc, Daily Herald |
60
+ | geopoliticallocation | 1016 | 1016 | Spicewood, Massillon, Northwestern Mexico |
61
+ | personus | 996 | 996 | James Brolin, Stephen Gyllenhaal, Kelly Preston |
62
+ | weapon | 932 | 0 | sized gas, wmd, mirv |
63
+ | coach | 924 | 924 | Gary Pinkel, Clinton White, Mike Gundy |
64
+ | journalist | 875 | 875 | Alan Cooperman, Elizabeth Kolbert, Rusty Anderson |
65
+ | room | 848 | 0 | galleried bedroom, style kitchen, bigger room |
66
+ | musicartist | 814 | 814 | Tomorrow, Threats, Circle Jerks |
67
+ | politicianus | 713 | 713 | Democrats Barack Obama, Military Personnel, Frederick Douglass |
68
+ | stadiumoreventvenue | 648 | 648 | Rebook Stadium, Balcones Heights, Boone Pickens Stadium |
69
+ | officebuildingroom | 636 | 0 | oversized guest rooms, 2 cabins, quadruple rooms |
70
+ | chemical | 620 | 0 | global warming pollution, lactose, floor |
71
+ | movie | 620 | 620 | Goldeneye, The Ten Commandments, Southland Tales |
72
+ | musician | 619 | 619 | Jeff Tweedy, Chuck Berry, Wayne Coyne |
73
+ | ceo | 619 | 619 | Hector Ruiz, Use All Rights, Michael J Critelli |
74
+ | university | 616 | 616 | Victoria College, Raytheon, Longwood |
75
+ | geopoliticalorganization | 612 | 612 | Devonshire, Clewiston, Kumasi |
76
+ | stateorprovince | 600 | 600 | Karnataka, Styria, St George |
77
+ | profession | 592 | 0 | mediators, agents, gastroenterologists |
78
+ | plant | 568 | 0 | china grass, tree ferns, deutzia |
79
+ | aquarium | 535 | 0 | 5 gallon saltwater aquarium, aquascape all glass aquarium, bor s djurpark |
80
+ | website | 533 | 533 | New Republic Magazine, Canadian, Lycos |
81
+ | county | 522 | 522 | Janesville, Denton City, Pocahontas |
82
+ | disease | 510 | 510 | Sunburn, Mental Health Issues, Behavioural Problems |
83
+ | male | 484 | 484 | Offense, Pilsen, Brisbane |
84
+ | radiostation | 476 | 476 | Job, Klrt, Geneva |
85
+ | country | 472 | 472 | Denmark, South West Asia, Latvia |
86
+ | jobposition | 458 | 0 | construction manager, governments, senior minister |
87
+ | agriculturalproduct | 449 | 0 | acai berry, root vegetables, vehicles |
88
+ | biotechcompany | 435 | 435 | Ortho Mcneil, Cameco, Chubb Corp |
89
+ | bakedgood | 430 | 0 | shakes, 2 cookies, biscuits |
90
+ | bank | 430 | 430 | Ual, Ing Direct, State Bank |
91
+ | hotel | 428 | 428 | Shangri La, Novotel, Algonquin Hotel |
92
+ | organization | 424 | 424 | Appletv, Sports Service, Year The Red Sox |
93
+ | actor | 418 | 418 | Financial, Posts, Marilyn Monroe |
94
+ | governmentorganization | 409 | 409 | High Government, US Federal Office, Immunization Branch Texas Department |
95
+ | personeurope | 402 | 402 | Arthur Miller, Line, Steven Greenhouse |
96
+ | food | 393 | 0 | feta, red fruits, chunkier foods |
97
+ | female | 390 | 390 | Paris Hilton, Balius, Terri Seymour |
98
+ | visualizablething | 386 | 386 | Kennebunk, Cruise, Marlton |
99
+ | personcanada | 365 | 365 | Matthew Perry, Sheryl Crow, Billy Gilman |
100
+ | newspaper | 357 | 357 | Evening Times, Art New England, Morning Herald |
101
+ | visualizablescene | 357 | 357 | Vallejo, River Edge, Wagrain |
102
+ | river | 344 | 344 | Wisconsin River, Rivanna, Mississippi River |
103
+ | politician | 342 | 342 | Andrew Miller, Senator Ted Kennedy, Julie Mason |
104
+ | attraction | 339 | 339 | The London Eye, Shakespeare Globe, St James Palace |
105
+ | politicsblog | 335 | 335 | Austin American, BBC News, Oc Register |
106
+ | animal | 312 | 0 | pets, parrots, waterbirds |
107
+ | celebrity | 307 | 307 | Eliza Dushku, Yellowcard, Marion Cotillard |
108
+ | blog | 306 | 0 | maya angelou, news com, news network |
109
+ | building | 304 | 304 | Dream, Place, Warhol Museum |
110
+ | mammal | 304 | 0 | designer dog, commonplace animals, north america |
111
+ | director | 299 | 299 | Vincent Minnelli, Stephen Gaghan, Fred Zinnemann |
112
+ | bodypart | 299 | 0 | chambers, atrial septum, dog s heart |
113
+ | scientificterm | 294 | 294 | Learning Invariances In Neural Nets, Cross Product, Sequential Minimal Optimization |
114
+ | personmexico | 274 | 274 | Bob Welch, David Garrard, Woody Johnson |
115
+ | island | 270 | 270 | Tango, Grand Island, Bay Of Plenty |
116
+ | personaustralia | 263 | 263 | Charles De Lint, Ford Madox Ford, Malcolm Lowry |
117
+ | transportation | 258 | 258 | Maglev, Lirr, Text |
118
+ | hobby | 258 | 0 | day trips, recreational opportunities, discussions |
119
+ | musicalbum | 257 | 257 | Two Days, Halo, Oceania |
120
+ | park | 247 | 247 | Shepherds Bush, Brockton, Sites |
121
+ | criminal | 243 | 243 | Blog Entries, Articles The 3, Lary Hoover |
122
+ | visualartist | 239 | 239 | Juan Gris, Rafael, Mattia Preti |
123
+ | airport | 236 | 236 | Cyprus, Charlotte Douglas International, North Las Vegas Airport |
124
+ | professor | 235 | 235 | Ian Flemming, Christopher Hitchens, One David |
125
+ | bathroomitem | 235 | 0 | showerhead, hot tub, large soaking tub |
126
+ | personnorthamerica | 235 | 235 | Ken Belson, Michael Albert, Florida Marlins |
127
+ | emotion | 230 | 230 | Lack, Lethargy, Type |
128
+ | school | 229 | 229 | Wayne State University, Wilberforce University, North Carolina State University |
129
+ | sportsgame | 226 | 226 | 1932 World Series, Days Last Year, 1905 World Series |
130
+ | buildingfeature | 216 | 216 | Users, Points, Front Doors |
131
+ | insect | 214 | 0 | parasitic wasps, young grubs, ladybugs |
132
+ | automobilemodel | 210 | 210 | Toyota Tacoma, 300zx, Custom 500 |
133
+ | furniture | 210 | 0 | berths, ashley furniture, bedding |
134
+ | publication | 205 | 205 | Dow Jones, News Corp, Mci |
135
+ | scientist | 204 | 204 | Epsilon, Jorge Luis Borges, Balmer |
136
+ | magazine | 203 | 203 | Harpers, Real People, Afghan Civilians |
137
+ | bird | 199 | 0 | granivorous, jacanas, large bird |
138
+ | product | 194 | 194 | Toshiba, Linux, Ms Windows |
139
+ | professionalorganization | 192 | 0 | interface, asi, healthcare reform |
140
+ | musicsong | 186 | 186 | Boys, God Of Thunder, Last Time |
141
+ | personafrica | 185 | 185 | Guillermo Del Toro, Cameron Crowe, Warner Baxter |
142
+ | automobilemaker | 177 | 177 | German Automakers, Yamaha, Gma |
143
+ | televisionshow | 176 | 176 | Concentration, Rambling Rose, Tribune |
144
+ | lake | 172 | 172 | Lake Bartlett, Crystal Lake, Lake Tahoe |
145
+ | museum | 168 | 168 | Home, Path, Forbes Galleries |
146
+ | artery | 167 | 167 | Arms, Hepatic Artery, Left Side |
147
+ | bacteria | 165 | 165 | P Falciparum, Parasite Trypanosoma Cruzi, Actinomyces Israelii |
148
+ | beverage | 164 | 0 | high efficiency water, breakfast, duet |
149
+ | language | 162 | 162 | Alemannic, Albanian, Castilian |
150
+ | comedian | 159 | 159 | Kate Douglas Wiggin, Jenny Mollen, Jean Jacques Annaud |
151
+ | politicsissue | 152 | 0 | nursing degree, resource services, journalism degree |
152
+ | mountain | 150 | 150 | Sierra Nevada, Geography, Southern Appalachian |
153
+ | landscapefeatures | 148 | 0 | deserts, sanctuary, managment |
154
+ | fish | 146 | 0 | starfish, sea bream, sea anemones |
155
+ | sportsequipment | 138 | 138 | Golf Shoes, Top, Bat |
156
+ | retailstore | 138 | 138 | Express Post, L L Bean Outdoor Discovery Schools, Stater Bros |
157
+ | musicinstrument | 135 | 135 | Acoustic Bass, Firo B, Drum |
158
+ | skiarea | 132 | 132 | Lake Erie, Fraser River, Oak Forest |
159
+ | programminglanguage | 131 | 131 | Credits, The New, Tips |
160
+ | software | 130 | 130 | System Works, Windows Server 2, Perfect Fit |
161
+ | winery | 126 | 126 | Windsor, Melville, Sanford |
162
+ | geometricshape | 125 | 125 | Theory, Degree, Regions |
163
+ | sport | 123 | 0 | area agencies, golf, professional football |
164
+ | location | 122 | 0 | convenient place, saipan, cosmopolitan areas |
165
+ | recordlabel | 121 | 121 | Cheap Trick, Columbia Records, Young Money Entertainment |
166
+ | physiologicalcondition | 121 | 0 | stomach ulcers, stress, medical illness |
167
+ | vertebrate | 120 | 0 | cattles, mountain lions, nature horses |
168
+ | physicalaction | 115 | 115 | Pakistan, Markets, Beaverton |
169
+ | personasia | 115 | 115 | Subpages, Family Members, Chat |
170
+ | musicgenre | 107 | 107 | Synth Pop, Dancehall, New Wave |
171
+ | vehicle | 103 | 0 | escape hybrid, countries, emergency vehicles |
172
+ | sportsteamposition | 102 | 102 | Offensive Lineman, Defender, Change |
173
+ | date | 101 | 0 | 1968, 1927, the end of the year |
174
+ | eventoutcome | 100 | 100 | Incredible Experience, Huge Impact, Corruption |
175
+ | vegetable | 99 | 0 | onion, radish, steamed vegetables |
176
+ | sportsleague | 98 | 98 | National Hockey League, Minors, Archives |
177
+ | arthropod | 96 | 0 | small invertebrates, woodlice, arthropods |
178
+ | politicalparty | 95 | 95 | Country Party, Gaming, House Water |
179
+ | politicaloffice | 95 | 95 | Secretary Of State, Senate President, Business Centers |
180
+ | awardtrophytournament | 95 | 95 | Super Bowl Title, Alan I Rothenberg Trophy, Medals |
181
+ | hallwayitem | 93 | 0 | panel, vent, free |
182
+ | model | 92 | 92 | Sarah Ban Breathnach, Jonathan Hunt, Ingrid |
183
+ | shoppingmall | 91 | 91 | Colonial Park, Caesars Palace, Manhattan Mall Shopping Center |
184
+ | terroristorganization | 87 | 87 | Palestinian Militants, Times, Incident |
185
+ | hospital | 86 | 86 | Plymouth Village, Prudential Center, Trinity Mother Frances |
186
+ | economicsector | 86 | 0 | search, loans insurance, insurance companies |
187
+ | braintissue | 86 | 86 | Temporal Lobe, 2, Brain Areas |
188
+ | color | 84 | 84 | Blend, Blue Pearl, Inspection |
189
+ | weatherphenomenon | 83 | 83 | Entire, Whirlwind, Thing |
190
+ | bedroomitem | 82 | 0 | order, full bath, marketplace |
191
+ | reptile | 81 | 0 | leopard, arrow, tree |
192
+ | chef | 80 | 80 | Jerome Jesus, Philippa Gregory, James Owen Smith |
193
+ | currency | 79 | 79 | Pesetas, Acres, Kwacha |
194
+ | invertebrate | 79 | 0 | mangora genus spider, wood storks, mammals |
195
+ | amphibian | 78 | 0 | crowned eagle, behavior, ferruginous hawk |
196
+ | monument | 77 | 77 | National Cathedral, Slidell, Los Alamos |
197
+ | charactertrait | 77 | 77 | Initiative, Prevention, World |
198
+ | street | 76 | 76 | Abercorn Street, Temple Street, College Street |
199
+ | visualizableobject | 72 | 72 | Jersey, Desktop Computer, Kickstand |
200
+ | restaurant | 69 | 69 | Fix, Peking Duck House Restaurant, Rite Aid Corporation |
201
+ | nongovorganization | 69 | 69 | U S, Sri Lankan Government, Lehi |
202
+ | wine | 69 | 0 | grains, johannisberg riesling, joint venture |
203
+ | buildingmaterial | 68 | 68 | Fiberglass, Stainless Steel, Filigree |
204
+ | videogame | 66 | 66 | Nights, Legend Of Zelda, Guitar Hero |
205
+ | nerve | 65 | 65 | Musculoskeletal System, Elbow, Human Brain |
206
+ | dateliteral | 65 | 0 | 1947, 1931, april 24 2013 |
207
+ | architect | 65 | 0 | albert, renzo piano, hart wood |
208
+ | monarch | 59 | 59 | Julian The Apostate, Single Mother, All Star |
209
+ | religion | 57 | 57 | Tabernacle, A Di Da Buddhist Temple, Thousand Years |
210
+ | videogamesystem | 56 | 56 | System Upgrades, Play Station, Os |
211
+ | condiment | 56 | 0 | red cabbage, bbq sauce, glaze |
212
+ | trainstation | 54 | 54 | Baltimore And Potomac Railroad Station, Allack Station, Ideal Base |
213
+ | mldataset | 54 | 54 | 38, 7, Senior |
214
+ | year | 53 | 53 | 1887, 1881, 1903 |
215
+ | personsouthamerica | 52 | 52 | Pepita Jimenez, Juan Valera, Michael Grant |
216
+ | kitchenitem | 52 | 0 | large refrigerator, staple, double oven |
217
+ | beach | 52 | 52 | Brandeis, Supply, W Palm Beach |
218
+ | astronaut | 51 | 51 | Discovery, Vance Brand, Charles Stross |
219
+ | crimeorcharge | 50 | 0 | credit, probation, violent crimes |
220
+ | parlourgame | 50 | 50 | The Man With The Twisted Lip, Six Hour Drive, Water |
221
+ | ethnicgroup | 50 | 50 | Soviet, Darling, Same Way |
222
+ | muscle | 50 | 50 | Hamstring, Transverse, Abdominal Muscles |
223
+ | farm | 49 | 49 | The United States, Puddicombe Farms, Orchard Valley Farms |
224
+ | nonprofitorganization | 49 | 49 | Haven, Humane Society, Drools |
225
+ | zoo | 49 | 49 | Denver Zoo, Saint Louis Zoo, Port Clinton |
226
+ | agent | 47 | 0 | anderson, derek powazek, liberty media corp |
227
+ | consumerelectronicitem | 47 | 47 | Ds Lite, Gba Sp, Ratings |
228
+ | tool | 47 | 47 | Checklists, Tools, Laundry Machine |
229
+ | port | 46 | 46 | Tampa International Airport, Kalmar, Ronald Reagan National Airport |
230
+ | mlauthor | 46 | 46 | Web Search, Alexander Doni, Yoram Singer |
231
+ | meat | 46 | 0 | aunt, flank steak, cream sauce |
232
+ | mollusk | 46 | 0 | rays, sea shells, find |
233
+ | crustacean | 45 | 0 | large image, lvmh, open directory project |
234
+ | musicfestival | 45 | 45 | Yamaha Music Festival, Wygant State Natural Area, William M Tugman State Park |
235
+ | tableitem | 43 | 0 | amr, registry, favor |
236
+ | planet | 42 | 42 | Result, Potpourri, Matter |
237
+ | conference | 41 | 41 | Bs, Uae, Strc |
238
+ | tradeunion | 40 | 0 | gmp, operators, legend |
239
+ | skyscraper | 40 | 40 | Compromise, Cn Tower, International City |
240
+ | protein | 40 | 40 | Insulin, Collagen, Laboratory |
241
+ | arachnid | 40 | 0 | movies, meetings, tick |
242
+ | nondiseasecondition | 39 | 39 | Job, System, Production |
243
+ | visualartform | 36 | 36 | Famous Painting, Perfume, Sculpture |
244
+ | grain | 35 | 0 | popcorn, jasmine rice, pole beans |
245
+ | visualartmovement | 34 | 34 | Surrealism, Dada, Britain |
246
+ | fruit | 34 | 0 | oranges, peaches, black berries |
247
+ | wallitem | 33 | 33 | Writing, Times, Transfer |
248
+ | candy | 33 | 0 | heart, candy corn, bounty |
249
+ | bone | 32 | 0 | two, human bone marrow, one arm |
250
+ | election | 30 | 30 | Same Name, Charles De Gaulle, Death |
251
+ | mlsoftware | 30 | 0 | vlc, spies, instance |
252
+ | highway | 30 | 30 | Four Miles, West Branch, First Place |
253
+ | event | 29 | 29 | Obtain, Tsunami Relief, Liquidity Crisis |
254
+ | placeofworship | 28 | 28 | Htilominlo Temple, Fort George, Wallington |
255
+ | nonneginteger | 28 | 28 | 90, 2, 4 |
256
+ | visualizableattribute | 27 | 27 | Dots, Huge, Cloud |
257
+ | boardgame | 27 | 27 | Winners, Truck, Risk |
258
+ | politicsbill | 26 | 26 | Climate Stewardship Acts, Patriot Act, Courtesy |
259
+ | householditem | 26 | 26 | Home Heating, Size Sofabed, Range |
260
+ | creditunion | 25 | 25 | Banks, Bad Credit Mortgage Loan, First State |
261
+ | perceptionaction | 25 | 25 | Iowa City, Committee Hearing, Ames |
262
+ | automobileengine | 24 | 24 | Ford, Publishing, Ford Escort |
263
+ | creativework | 24 | 24 | Bad Dreams, Bedtime Stories, Evil Dead |
264
+ | mlarea | 22 | 22 | Roles, Conditional Random Fields, Graphical Models |
265
+ | nut | 22 | 0 | year s, sesame seeds, living |
266
+ | race | 22 | 22 | Queensland, Morbihan, July 2 |
267
+ | personalcareitem | 22 | 22 | Tent, Wash, Brush |
268
+ | mlalgorithm | 22 | 22 | Design, 2 0, Sons |
269
+ | vein | 21 | 21 | Forage, Viscera, Adipose |
270
+ | sociopolitical | 21 | 21 | Output, Development, Government |
271
+ | researchproject | 21 | 21 | Science, Feasibility Study, Education Research |
272
+ | bridge | 20 | 20 | High Bridge, Honor, Millennium Bridge |
273
+ | archaea | 20 | 20 | Sulfolobus Acidocaldarius, Halobacteria, A |
274
+ | cheese | 19 | 19 | Jack Cheese, Ricotta Cheese, Fontina Cheese |
275
+ | legume | 19 | 0 | washing machine, string beans, southwestern |
276
+ | mountainrange | 17 | 17 | Appalachian, West Hills, Eagle Creek |
277
+ | physicalcharacteristic | 17 | 17 | Humanity, Strictest, Foray |
278
+ | cardgame | 16 | 16 | Taking, UK, Free |
279
+ | medicalprocedure | 16 | 16 | Cpr, Chamber, Oxygen |
280
+ | sportsevent | 16 | 16 | Short Months, Decade, Please Contact |
281
+ | militaryconflict | 15 | 15 | Six Day War, Great War, War |
282
+ | mlmetric | 15 | 15 | 9 11, Sums, Variation |
283
+ | petroleumrefiningcompany | 14 | 14 | Valero, Bp America, Ultramar Diamond Shamrock |
284
+ | celltype | 14 | 14 | Liver, Women S Hospital, Capacity |
285
+ | traditionalgame | 13 | 13 | Horseshoe Casino, 3 5, Daughters |
286
+ | mlconference | 13 | 13 | Interview, Kingman, Services The Ability |
287
+ | convention | 12 | 12 | Conversation, Technorati, Condition |
288
+ | meetingeventtitle | 12 | 12 | Brain Maps To Mechanisms Neural Circuit Molecular Architecture, Robot Futures, Math Monkeys And The Developing Brain |
289
+ | trail | 11 | 11 | In New Jersey, Miller Park, In Philadelphia |
290
+ | 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 |
291
+ | judge | 11 | 11 | Ny, Times Union, Parsons |
292
+ | physicsterm | 10 | 10 | Momentum, 19 9 Percent, 15 Miles |
293
+ | coffeedrink | 10 | 0 | coffee, mocha, turkish coffee |
294
+ | cognitiveactions | 10 | 10 | Goal, Abilities, Learning |
295
+ | zipcode | 10 | 10 | 19103, 33130, 77063 |
296
+ | mediatype | 10 | 10 | Databases, Irrigation, Medium |
297
+ | month | 10 | 0 | december, november, march |
298
+ | perceptionevent | 9 | 9 | Beep, Macromedia Flash, Light |
299
+ | cave | 8 | 0 | san gabriel, will, ie |
300
+ | lymphnode | 8 | 8 | Healthy Eating, Lymph Nodes, Lymph System |
301
+ | continent | 7 | 7 | European Continent, South America, Europe |
302
+ | filmfestival | 7 | 7 | Beginning, Igoogle, New York Times Company |
303
+ | officeitem | 7 | 7 | Capacities, Policy Page, Credit Card Click |
304
+ | fungus | 7 | 0 | compound, cook, enrichment |
305
+ | url | 6 | 6 | Direct Links, Numerous Links, Links Links |
306
+ | olympics | 6 | 6 | CBS Television Network, Palestinian National Authority, Mayor |
307
+ | game | 6 | 6 | Aspects, Designee The Vice, Nice 3 |
308
+ | flooritem | 5 | 5 | Storage Tank, Waste Management, Water Quality |
309
+ | televisionnetwork | 5 | 5 | Cw, PBS, Independent |
310
+ | time | 4 | 4 | 8 30 P M, 7 Click, 4 00 P M Contact |
311
+ | mediacompany | 4 | 4 | Vibrant Media, Media Public, Simplify Media |
312
+ | dayofweek | 4 | 4 | Monday, Wednesday, Tuesday |
313
+ | caf_ | 3 | 3 | Starbucks Coffee Company, Sbux, Tim Hortons |
314
+ | victim | 3 | 3 | Resources Students, Health Sciences Students, Technology Students |
315
+ | militaryeventtype | 3 | 3 | Regimes, Attacks, Clashes |
316
+ | item | 3 | 3 | Laundry Detergent, Laptop, Dishes |
317
+ | geolocatablething | 3 | 3 | Buildings, Iquique, Cars |
318
+ | politicsgroup | 2 | 2 | Description, Moveon Org |
319
+ | gamescore | 2 | 2 | 4 5, 2 3 |
320
+ | refineryproduct | 2 | 2 | Coolant, East Coast Ports |
321
+ | grandprix | 2 | 2 | Renault, Bernie Ecclestone |
322
+ | personbylocation | 1 | 1 | Dan Garton |
323
+ | species | 1 | 1 | Marine Flora |
324
+ | humanagent | 1 | 1 | Monsanto Co S G D Searle Division |
325
+ | virus | 1 | 1 | Safe Drinking Water |
326
+ | recipe | 1 | 1 | Rice |
327
+ | SUM | 68518 | 53887 | |
328
 
329
+ - Head/Tail Entity Distribution
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
330
 
331
+ | entity_type | nell (head) | nell_filter (head) | nell (tail) | nell_filter (tail) |
332
+ |:-------------------------|--------------:|---------------------:|--------------:|---------------------:|
333
+ | politicianus | 352 | 352 | 360 | 360 |
334
+ | sportsteam | 295 | 295 | 0 | 0 |
335
+ | automobilemaker | 274 | 273 | 54 | 54 |
336
+ | insect | 230 | 0 | 270 | 0 |
337
+ | automobilemodel | 100 | 100 | 0 | 0 |
338
+ | animal | 97 | 0 | 30 | 0 |
339
+ | sport | 93 | 0 | 74 | 0 |
340
+ | agriculturalproduct | 87 | 0 | 0 | 0 |
341
+ | sportsgame | 74 | 0 | 0 | 0 |
342
+ | product | 62 | 62 | 0 | 0 |
343
+ | software | 42 | 42 | 0 | 0 |
344
+ | city | 42 | 42 | 161 | 161 |
345
+ | stateorprovince | 38 | 38 | 0 | 0 |
346
+ | athlete | 34 | 0 | 59 | 59 |
347
+ | organization | 32 | 32 | 2 | 2 |
348
+ | arthropod | 32 | 0 | 41 | 0 |
349
+ | beverage | 27 | 0 | 0 | 0 |
350
+ | country | 27 | 27 | 317 | 91 |
351
+ | geopoliticallocation | 24 | 24 | 14 | 8 |
352
+ | mammal | 23 | 0 | 0 | 0 |
353
+ | politician | 23 | 23 | 58 | 58 |
354
+ | person | 14 | 0 | 96 | 96 |
355
+ | invertebrate | 14 | 0 | 43 | 0 |
356
+ | personmexico | 13 | 0 | 20 | 20 |
357
+ | crustacean | 11 | 0 | 25 | 0 |
358
+ | coffeedrink | 11 | 0 | 0 | 0 |
359
+ | school | 11 | 11 | 0 | 0 |
360
+ | hobby | 10 | 0 | 0 | 0 |
361
+ | county | 10 | 10 | 4 | 4 |
362
+ | vegetable | 8 | 0 | 0 | 0 |
363
+ | reptile | 4 | 0 | 0 | 0 |
364
+ | personaustralia | 4 | 0 | 5 | 5 |
365
+ | videogame | 4 | 4 | 0 | 0 |
366
+ | food | 4 | 0 | 0 | 0 |
367
+ | celebrity | 4 | 4 | 2 | 2 |
368
+ | male | 3 | 1 | 5 | 5 |
369
+ | female | 3 | 3 | 3 | 3 |
370
+ | visualizablescene | 3 | 3 | 3 | 3 |
371
+ | grain | 2 | 0 | 0 | 0 |
372
+ | vehicle | 2 | 0 | 0 | 0 |
373
+ | amphibian | 2 | 0 | 0 | 0 |
374
+ | legume | 1 | 0 | 0 | 0 |
375
+ | company | 1 | 1 | 147 | 144 |
376
+ | arachnid | 1 | 0 | 6 | 0 |
377
+ | coach | 1 | 0 | 245 | 245 |
378
+ | mlsoftware | 1 | 0 | 0 | 0 |
379
+ | fruit | 1 | 0 | 0 | 0 |
380
+ | professor | 1 | 1 | 0 | 0 |
381
+ | island | 1 | 1 | 0 | 0 |
382
+ | chemical | 1 | 0 | 0 | 0 |
383
+ | personus | 1 | 1 | 6 | 6 |
384
+ | personnorthamerica | 1 | 0 | 3 | 3 |
385
+ | geopoliticalorganization | 1 | 1 | 17 | 7 |
386
+ | drug | 1 | 1 | 0 | 0 |
387
+ | planet | 0 | 0 | 1 | 1 |
388
+ | bodypart | 0 | 0 | 69 | 0 |
389
+ | criminal | 0 | 0 | 1 | 1 |
390
+ | director | 0 | 0 | 1 | 1 |
391
+ | personeurope | 0 | 0 | 1 | 1 |
392
+ | astronaut | 0 | 0 | 1 | 1 |
393
+ | journalist | 0 | 0 | 1 | 1 |
394
+ | location | 0 | 0 | 2 | 0 |
395
+ | biotechcompany | 0 | 0 | 11 | 10 |
396
 
397
+ - Relation Size
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
398
 
399
+ | relation_type | nell | nell_filter |
400
+ |:-----------------------------------------------|-------:|--------------:|
401
+ | concept:animalsuchasinvertebrate | 415 | 0 |
402
+ | concept:politicianusendorsespoliticianus | 386 | 386 |
403
+ | concept:teamcoach | 341 | 341 |
404
+ | concept:producedby | 213 | 209 |
405
+ | concept:automobilemakerdealersincity | 178 | 177 |
406
+ | concept:geopoliticallocationresidenceofpersion | 143 | 143 |
407
+ | concept:agriculturalproductcamefromcountry | 140 | 0 |
408
+ | concept:sportschoolincountry | 103 | 0 |
409
+ | concept:automobilemakerdealersincountry | 96 | 96 |
410
+ | concept:sportsgamesport | 74 | 0 |
411
+ | concept:athleteinjuredhisbodypart | 69 | 0 |
412
 
413
  ### Citation Information
414
  ```
data/nell.vocab.raw.txt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:29af9862df4777ce613d72e94c460ade35e60477704fa7a48274cc803ca7ea4f
3
+ size 2114701
data/nell.vocab.txt CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:29af9862df4777ce613d72e94c460ade35e60477704fa7a48274cc803ca7ea4f
3
- size 2114701
 
1
  version https://git-lfs.github.com/spec/v1
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+ oid sha256:5bd311c8a7cfb14a8625cec2f2ef7dfdb26a01e29a45a02dabc789873fb9dcd2
3
+ size 1562449
data/nell_filter.vocab.txt CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:52489b95fececef828e13cf875c86128ab3b8134c6bdbce7618d809ee59a75f5
3
- size 694324
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:c1d50981aab376eee6bc0cc41a38228b5c0bf5a2ac7501070268c2a724f71a22
3
+ size 1225922
data/wiki.vocab.raw.txt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:8491e7a8cd96a74c74667adfa3a8403d6040a1aaf265f8b8d2161fb2c684d119
3
+ size 46168647
generate_filtered_data.py CHANGED
@@ -79,12 +79,9 @@ for s in ["train", "validation", "test"]:
79
 
80
 
81
  with open("data/nell.vocab.txt") as f:
82
- types = [i for i in f.read().split('\n')]
83
 
84
- with open("data/nell.vocab.clean.txt") as f:
85
- vocab = [i for i in f.read().split('\n')]
86
- vocab_type = [(a, b.split(":")[1]) for a, b in zip(vocab, types) if len(a) > 0 and len(b) > 0 and len(b.split(":")) > 2]
87
- vocab_new = [a for a, b in vocab_type if b not in non_entity_types]
88
  with open("data/nell_filter.vocab.txt", 'w') as f:
89
- f.write('\n'.join(vocab_new))
90
 
 
79
 
80
 
81
  with open("data/nell.vocab.txt") as f:
82
+ vocab = [i.split("\t") for i in f.read().split('\n')]
83
 
84
+ vocab = ["\t".join([a, b]) for a, b in vocab if b not in non_entity_types]
 
 
 
85
  with open("data/nell_filter.vocab.txt", 'w') as f:
86
+ f.write('\n'.join(vocab))
87
 
process.py CHANGED
@@ -138,14 +138,15 @@ def read_vocab(_file):
138
 
139
  if __name__ == '__main__':
140
  vocab = read_vocab(f"{data_dir_nell}/ent2ids")
 
 
 
 
141
  with open("data/nell.vocab.txt", 'w') as f:
142
  f.write("\n".join(vocab))
143
- vocab_clean = [clean(i)[0] if len(i.split(":")) > 2 else i for i in vocab]
144
- with open("data/nell.vocab.clean.txt", 'w') as f:
145
- f.write("\n".join(vocab_clean))
146
 
147
  vocab = read_vocab(f"{data_dir_wiki}/ent2ids")
148
- with open("data/wiki.vocab.txt", 'w') as f:
149
  f.write("\n".join(vocab))
150
 
151
  for i, s in zip(['dev_tasks.json', 'test_tasks.json', 'train_tasks.json'], ['validation', 'test', 'train']):
 
138
 
139
  if __name__ == '__main__':
140
  vocab = read_vocab(f"{data_dir_nell}/ent2ids")
141
+ with open("data/nell.vocab.raw.txt", 'w') as f:
142
+ f.write("\n".join(vocab))
143
+ vocab = [clean(i) for i in vocab if len(i.split(":")) > 2]
144
+ vocab = ["\t".join(i) for i in vocab if len(i[0]) > 0 and len(i[1]) > 0]
145
  with open("data/nell.vocab.txt", 'w') as f:
146
  f.write("\n".join(vocab))
 
 
 
147
 
148
  vocab = read_vocab(f"{data_dir_wiki}/ent2ids")
149
+ with open("data/wiki.vocab.raw.txt", 'w') as f:
150
  f.write("\n".join(vocab))
151
 
152
  for i, s in zip(['dev_tasks.json', 'test_tasks.json', 'train_tasks.json'], ['validation', 'test', 'train']):
stats.py CHANGED
@@ -1,22 +1,72 @@
1
  import pandas as pd
 
2
  from datasets import load_dataset
 
3
 
 
4
  for _type in ['nell', 'nell_filter']:
5
- data = load_dataset("relbert/fewshot_link_prediction", _type, split='test')
6
- df = data.to_pandas()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7
 
8
- print(f"\nEntity Types ({_type})")
9
- tail = df.groupby("tail_type")['relation'].count().to_dict()
10
- head = df.groupby("head_type")['relation'].count().to_dict()
11
- k = set(list(tail.keys()) + list(head.keys()))
12
- df_types = pd.DataFrame([{"entity_type": _k, "tail": tail[_k] if _k in tail else 0, "head": head[_k] if _k in head else 0} for _k in k])
13
- print(df_types.to_markdown(index=False))
14
 
15
- print(f"\nRelation Types ({_type})")
16
- print(df.groupby("relation")['relation'].count().to_markdown())
 
 
17
 
18
- print(f"\nVocab Size ({_type})")
19
- with open(f"data/{_type}.vocab.txt") as f:
20
- length = len([i for i in f.read().split('\n') if len(i) > 0])
21
- print(length)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
22
 
 
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 ['nell', 'nell_filter']:
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)
11
+ vocab_df['sample'] = [
12
+ ", ".join(vocab[vocab.type == i].sample(min(3, sum(vocab.type == i)))["entity"].values.tolist()) for i in
13
+ vocab_df.index]
14
+ tmp[_type] = vocab_df
15
+ keys = set(list(chain(*[list(v.index) for v in tmp.values()])))
16
+ df = pd.DataFrame([{
17
+ "entity_type": k,
18
+ "nell": tmp["nell"].loc[k]['entity'] if k in tmp["nell"].index else 0,
19
+ "nell_filter": tmp["nell_filter"].loc[k]['entity'] if k in tmp["nell_filter"].index else 0,
20
+ "sample": tmp["nell"].loc[k]['sample'] if k in tmp["nell"].index else tmp["nell_filter"].loc[k]['sample']
21
+ } for k in keys]).sort_values(by="nell", ascending=False)
22
+ df = pd.concat([df, pd.DataFrame([{
23
+ "entity_type": "SUM",
24
+ 'nell': df['nell'].sum(),
25
+ 'nell_filter': df['nell_filter'].sum(),
26
+ 'sample': ""}])])
27
+ df.to_csv(f"stats/stats.vocab.csv", index=False)
28
+ print(f"\nVocab Size")
29
+ print(df.to_markdown(index=False))
30
 
31
+ for split in ['train', 'validation', 'test']:
32
+ print(split)
 
 
 
 
33
 
34
+ tmp = {}
35
+ for _type in ['nell', 'nell_filter']:
36
+ data = load_dataset("relbert/fewshot_link_prediction", _type, split=split)
37
+ df = data.to_pandas()
38
 
39
+ tail = df.groupby("tail_type")['relation'].count().to_dict()
40
+ head = df.groupby("head_type")['relation'].count().to_dict()
41
+ k = set(list(tail.keys()) + list(head.keys()))
42
+ df_types = pd.DataFrame([{"entity_type": _k, "tail": tail[_k] if _k in tail else 0, "head": head[_k] if _k in head else 0} for _k in k])
43
+ df_types.index = df_types.pop("entity_type")
44
+ tmp[_type] = df_types
45
+
46
+ keys = set(list(chain(*[list(v.index) for v in tmp.values()])))
47
+ df = pd.DataFrame([{
48
+ "entity_type": k,
49
+ "nell (head)": tmp["nell"].loc[k]['head'] if k in tmp["nell"].index else 0,
50
+ "nell_filter (head)": tmp["nell_filter"].loc[k]['head'] if k in tmp["nell_filter"].index else 0,
51
+ "nell (tail)": tmp["nell"].loc[k]['tail'] if k in tmp["nell"].index else 0,
52
+ "nell_filter (tail)": tmp["nell_filter"].loc[k]['tail'] if k in tmp["nell_filter"].index else 0,
53
+ } for k in keys]).sort_values(by="nell (head)", ascending=False)
54
+ df.to_csv(f"stats/stats.{split}.entity.csv", index=False)
55
+ print(f"\nHead/Tail Size")
56
+ print(df.to_markdown(index=False))
57
+
58
+ tmp = {}
59
+ for _type in ['nell', 'nell_filter']:
60
+ data = load_dataset("relbert/fewshot_link_prediction", _type, split=split)
61
+ df = data.to_pandas()
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,
66
+ "nell": tmp["nell"].loc[k] if k in tmp["nell"].index else 0,
67
+ "nell_filter": tmp["nell_filter"].loc[k] if k in tmp["nell_filter"].index else 0,
68
+ } for k in keys]).sort_values(by="nell", ascending=False)
69
+ df.to_csv(f"stats/stats.{split}.relation.csv", index=False)
70
+ print(f"\nRelation Size")
71
+ print(df.to_markdown(index=False))
72
 
stats/stats.test.entity.csv ADDED
@@ -0,0 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ entity_type,nell (head),nell_filter (head),nell (tail),nell_filter (tail)
2
+ politicianus,352,352,360,360
3
+ sportsteam,295,295,0,0
4
+ automobilemaker,274,273,54,54
5
+ insect,230,0,270,0
6
+ automobilemodel,100,100,0,0
7
+ animal,97,0,30,0
8
+ sport,93,0,74,0
9
+ agriculturalproduct,87,0,0,0
10
+ sportsgame,74,0,0,0
11
+ product,62,62,0,0
12
+ software,42,42,0,0
13
+ city,42,42,161,161
14
+ stateorprovince,38,38,0,0
15
+ athlete,34,0,59,59
16
+ arthropod,32,0,41,0
17
+ organization,32,32,2,2
18
+ beverage,27,0,0,0
19
+ country,27,27,317,91
20
+ geopoliticallocation,24,24,14,8
21
+ mammal,23,0,0,0
22
+ politician,23,23,58,58
23
+ person,14,0,96,96
24
+ invertebrate,14,0,43,0
25
+ personmexico,13,0,20,20
26
+ coffeedrink,11,0,0,0
27
+ school,11,11,0,0
28
+ crustacean,11,0,25,0
29
+ county,10,10,4,4
30
+ hobby,10,0,0,0
31
+ vegetable,8,0,0,0
32
+ personaustralia,4,0,5,5
33
+ videogame,4,4,0,0
34
+ reptile,4,0,0,0
35
+ celebrity,4,4,2,2
36
+ food,4,0,0,0
37
+ male,3,1,5,5
38
+ visualizablescene,3,3,3,3
39
+ female,3,3,3,3
40
+ vehicle,2,0,0,0
41
+ amphibian,2,0,0,0
42
+ grain,2,0,0,0
43
+ arachnid,1,0,6,0
44
+ drug,1,1,0,0
45
+ personus,1,1,6,6
46
+ professor,1,1,0,0
47
+ legume,1,0,0,0
48
+ coach,1,0,245,245
49
+ chemical,1,0,0,0
50
+ company,1,1,147,144
51
+ mlsoftware,1,0,0,0
52
+ island,1,1,0,0
53
+ geopoliticalorganization,1,1,17,7
54
+ personnorthamerica,1,0,3,3
55
+ fruit,1,0,0,0
56
+ planet,0,0,1,1
57
+ biotechcompany,0,0,11,10
58
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+ criminal,0,0,1,1
stats/stats.test.relation.csv ADDED
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1
+ relation_type,nell,nell_filter
2
+ concept:animalsuchasinvertebrate,415,0
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+ concept:politicianusendorsespoliticianus,386,386
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+ concept:teamcoach,341,341
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+ concept:producedby,213,209
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+ concept:automobilemakerdealersincity,178,177
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+ concept:geopoliticallocationresidenceofpersion,143,143
8
+ concept:agriculturalproductcamefromcountry,140,0
9
+ concept:sportschoolincountry,103,0
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+ concept:automobilemakerdealersincountry,96,96
11
+ concept:sportsgamesport,74,0
12
+ concept:athleteinjuredhisbodypart,69,0
stats/stats.train.entity.csv ADDED
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1
+ entity_type,nell (head),nell_filter (head),nell (tail),nell_filter (tail)
2
+ insect,782,0,904,0
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148
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+ room,0,0,10,0
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+ politicaloffice,0,0,216,216
stats/stats.train.relation.csv ADDED
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1
+ relation_type,nell,nell_filter
2
+ concept:athleteledsportsteam,424,424
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8
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13
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14
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20
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22
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23
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24
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25
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26
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27
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28
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29
+ concept:fooddecreasestheriskofdisease,122,1
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+ concept:personleadsgeopoliticalorganization,120,120
31
+ concept:stateorprovinceoforganizationheadquarters,120,118
32
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33
+ concept:itemexistsatlocation,110,0
34
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35
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36
+ concept:countriessuchascountries,100,100
37
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38
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39
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40
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41
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42
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43
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44
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45
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46
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47
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48
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49
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50
+ concept:vegetableproductioninstateorprovince,56,0
51
+ concept:clothingmadefromplant,54,0
52
+ concept:organizationdissolvedatdate,51,0
stats/stats.validation.entity.csv ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ entity_type,nell (head),nell_filter (head),nell (tail),nell_filter (tail)
2
+ city,316,316,316,316
3
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4
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9
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+ personsouthamerica,0,0,17,17
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+ country,0,0,198,197
stats/stats.validation.relation.csv ADDED
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1
+ relation_type,nell,nell_filter
2
+ concept:cityalsoknownas,356,356
3
+ concept:bankbankincountry,230,229
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+ concept:parentofperson,217,217
5
+ concept:sportusesstadium,125,0
6
+ concept:politicalgroupofpoliticianus,76,76
stats/stats.vocab.csv ADDED
@@ -0,0 +1,280 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ entity_type,nell,nell_filter,sample
2
+ person,4026,4026,"George P Schultz, Arthur, Les Miserables"
3
+ book,3835,3835,"The Heart Of The Matter, Leadership, Battlefield Earth"
4
+ city,3020,3020,"Kyoto, Maryville, Name"
5
+ athlete,2965,2965,"Linas Kleiza, Josh Butler, Jose Acevedo"
6
+ company,2520,2520,"Dialogic, Kfqx, Kdse"
7
+ sportsteam,1799,1799,"Crew, Winston Salem State Rams, Se Missouri State Indians"
8
+ clothing,1589,0,"calico, mini skirt, bag"
9
+ writer,1345,1345,"Max Frisch, Michael J Fox, Joyce Grenfell"
10
+ academicfield,1154,0,"resource management, wellness, communications studies"
11
+ drug,1030,1030,"Diltiazem, Tambocor, Trazodone"
12
+ televisionstation,1018,1018,"Wutb, Wfft Tv, Wvny Tv"
13
+ geopoliticallocation,1016,1016,"Baltic States, Macau, Sides"
14
+ personus,996,996,"Gore Vidal, Khaled Hosseini, Tommy Shaw"
15
+ weapon,932,0,"gas imports, battle ready swords, pulse rifles"
16
+ coach,924,924,"Shanahan, Jeff Turner, Washington Redskins"
17
+ journalist,875,875,"Mara Liasson, Patient, Scott Bordow"
18
+ room,848,0,"study, open plan bedroom, second living area"
19
+ musicartist,814,814,"Howard, Black Uhuru, Michael Schenker"
20
+ politicianus,713,713,"Kevin Johnson, Dianne Feinstein, Richard Daley"
21
+ stadiumoreventvenue,648,648,"Coliseum, Scottrade Center, Nippert Stadium"
22
+ officebuildingroom,636,0,"one bedroom cottages, third guest bedroom, deluxe master bath"
23
+ movie,620,620,"City Of God, Rope, Halloween"
24
+ chemical,620,0,"atmospheric pollution, alkaline, oil"
25
+ ceo,619,619,"Kerry Killinger, Herbert Hainer, Use Send"
26
+ musician,619,619,"Tom Morello, Elgar, Steve Lacy"
27
+ university,616,616,"Leiden University, Fiu, Hebrew Union College Jewish Institute Of Religion"
28
+ geopoliticalorganization,612,612,"Pacific Grove, Waxahachie, Ibaraki Prefecture"
29
+ stateorprovince,600,600,"Himachal Pradesh, Kashmir, Massachussetts"
30
+ profession,592,0,"neurosurgeons, professional staff, sales"
31
+ plant,568,0,"ancient oak, water oaks, skunk cabbage"
32
+ aquarium,535,0,"saarbrucken zoo, turtle aquarium, 500 gallon aquarium"
33
+ website,533,533,"Www Philly Com Inquirer, Memory, News Search"
34
+ county,522,522,"Korea University, Boise City, Hopkinton"
35
+ disease,510,510,"Headaches, Ain, Illness"
36
+ male,484,484,"Library, Rick Davis, Gym Class Heroes"
37
+ radiostation,476,476,"Wclj, Kvct, Knbr"
38
+ country,472,472,"Netherland, Bfpo Addresses, Top States"
39
+ jobposition,458,0,"ballerina, senior associate, economist"
40
+ agriculturalproduct,449,0,"oilseeds, butter, mint leaves"
41
+ biotechcompany,435,435,"Elan Pharmaceuticals, Bache Halsey Stuart Shields, Agouron Pharmaceutical"
42
+ bank,430,430,"Bayern, Wachovia Securities, Cs First Boston"
43
+ bakedgood,430,0,"sponge cake, puddings, hay"
44
+ hotel,428,428,"Sands, Holiday Inn Express, London House"
45
+ organization,424,424,"Orbimed Advisors, Protection The Department, General Electric Co"
46
+ actor,418,418,"John Travolta, Sally Field, Uma Thurman"
47
+ governmentorganization,409,409,"States Supreme Court, California Department, Democratic National Committee"
48
+ personeurope,402,402,"Noel Streatfield, Pat Robertson, Jeffrey Archer"
49
+ food,393,0,"mix, sausages, sucker"
50
+ female,390,390,"Hera, Mary, Myla Goldberg"
51
+ visualizablething,386,386,"Pesto, Bentley Gt, Resource Revenues"
52
+ personcanada,365,365,"Michael Hyatt, Dan Harris, Self"
53
+ visualizablescene,357,357,"Birthplace, Hyannis, Contest"
54
+ newspaper,357,357,"Business News, Krqe Tv, New York Times"
55
+ river,344,344,"Rogue River, Coco River, Mandovi"
56
+ politician,342,342,"Alan Keyes, Aid, Representative George Miller"
57
+ attraction,339,339,"Secred Garden, Kimmel Center, La Aurora International Airport"
58
+ politicsblog,335,335,"Daily Record, New Statesman, Service Corps Of Retired Executives"
59
+ animal,312,0,"pet ferrets, goat, racoons"
60
+ celebrity,307,307,"Seton Hall Pirates, Wilson Pickett, Harvard Crimson"
61
+ blog,306,0,"christian broadcasting network, cnet, sunday express"
62
+ building,304,304,"Las Vegas Hilton, White House, Baker Berry Library"
63
+ mammal,304,0,"1 million children, boomtown rats, buffalo"
64
+ director,299,299,"Quentin Tarantino, John Schlesinger, Vincenzo Natali"
65
+ bodypart,299,0,"organ systems, system, genital area"
66
+ scientificterm,294,294,"Conditional Expectation, Probabilistic Pca, Support Vector Regression"
67
+ personmexico,274,274,"Ollie Linton, Justin Bass, Fernando Cabrera"
68
+ island,270,270,"Santa Cruz Del Islote, Obwalden, Joint Chiefs"
69
+ personaustralia,263,263,"Brent Johnson, Malcolm Lowry, David Muir"
70
+ hobby,258,0,"winter sports, writers, skydiving"
71
+ transportation,258,258,"Fare, Seatac Airport, Newark International Airport"
72
+ musicalbum,257,257,"Martin, Years, Compilation"
73
+ park,247,247,"Rides, Dulwich, Salford Quays"
74
+ criminal,243,243,"Mark Bryan, John Brown, Carlinhos Beira Mar"
75
+ visualartist,239,239,"Diego, Georges Seurat, Francis Ford Coppola"
76
+ airport,236,236,"Air Canada, Eleftherios Venizelos, Portland International"
77
+ professor,235,235,"Nancy Thomas, David Gest, James M Cain"
78
+ personnorthamerica,235,235,"David M Herszenhorn, Rediff, Robert F Wagner"
79
+ bathroomitem,235,0,"soaking tub, tub upstairs, lock"
80
+ emotion,230,230,"Love, Charity, Defiance"
81
+ school,229,229,"Southern Utah University, Rensselaer Polytechnic Institute, Berea College"
82
+ sportsgame,226,226,"Alds, 1999 World Series, 1982 World Series"
83
+ buildingfeature,216,216,"Change, Large Windows, Improvement"
84
+ insect,214,0,"leafminers, mealworms, chopin"
85
+ furniture,210,0,"large double bed, settee, sets glass top dining sets"
86
+ automobilemodel,210,210,"Sunliner, Edge, Taurus X"
87
+ publication,205,205,"Financial Mail, Data Domain, Industry Standard"
88
+ scientist,204,204,"Calvin, Klaus, Thomas Kuhn"
89
+ magazine,203,203,"Fitness, Wwd, Southern Living"
90
+ bird,199,0,"old world flycatchers, migratory waterfowl, black widow"
91
+ product,194,194,"Samsung Electronics Co Ltd, Microsoft Windows Ce, Illustrator"
92
+ professionalorganization,192,0,"maryland division, restaurant and catering association, science foundation"
93
+ musicsong,186,186,"Constellation, Download, Any Way You Want Me"
94
+ personafrica,185,185,"Caterpillar, Margaret Wise Brown, Anna Politkovskaya"
95
+ automobilemaker,177,177,"Honda, Porsche, Used Car Search"
96
+ televisionshow,176,176,"Mission, Jail, Prime Time"
97
+ lake,172,172,"Lake Granby, Smith Mountain Lake, Lake Pend Oreille"
98
+ museum,168,168,"New York Stock Exchange, Art, California Science Center"
99
+ artery,167,167,"Vascular System, Pulmonary Valve, Major Artery"
100
+ bacteria,165,165,"Echinococcus Multilocularis, Borrelia Recurrentis, Clostridium Botulinum"
101
+ beverage,164,0,"sophisticated water, soymilk, mixer"
102
+ language,162,162,"Gujarati, Creole, Kinyarwanda"
103
+ comedian,159,159,"Warren Zevon, Clive Barker, Bob Young"
104
+ politicsissue,152,0,"fiscal policy, resource services, health services"
105
+ mountain,150,150,"Diamond Head, Pleasant Ridge, Great Smoky Mountain"
106
+ landscapefeatures,148,0,"green pastures, hot water, renaissance"
107
+ fish,146,0,"starfish, tilapia, pelagic fish"
108
+ retailstore,138,138,"U S Post Office, Mcgraw Hill, Sam Walton"
109
+ sportsequipment,138,138,"West, Instructions, Ball"
110
+ musicinstrument,135,135,"Rock Band, Fortepiano, Menu"
111
+ skiarea,132,132,"Forked River, Minnesota River, Alta"
112
+ programminglanguage,131,131,"Enter, Montpellier, Site"
113
+ software,130,130,"Adobe Golive, Nemo, Excel 2"
114
+ winery,126,126,"Legacy, Rockland Farms Winery, Harvest"
115
+ geometricshape,125,125,"Units, Rates, Cone"
116
+ sport,123,0,"swimming, fantasy football, snorkelling"
117
+ location,122,0,"home water, representative, ideal location"
118
+ physiologicalcondition,121,0,"medical illness, male impotence, chronic arthritis"
119
+ recordlabel,121,121,"Blue Note, Top Rank, Emi Group"
120
+ vertebrate,120,0,"sugar gliders, animals people, tyrant flycatchers"
121
+ physicalaction,115,115,"Purpose, Share, Fusion"
122
+ personasia,115,115,"Chris Volstad, Gauri Khan, Ian Kinsler"
123
+ musicgenre,107,107,"Glam Metal, British Folk, Power Pop"
124
+ vehicle,103,0,"pumps, forklifts, animals"
125
+ sportsteamposition,102,102,"Defensive Tackle, Hitters, League"
126
+ date,101,0,"republic last year, the early 1990s, leonardo"
127
+ eventoutcome,100,100,"Outcome, Triumph, Sales"
128
+ vegetable,99,0,"peas, sweet potato, carrot"
129
+ sportsleague,98,98,"Partners, Sports, Uefa"
130
+ arthropod,96,0,"buckeye, verruca stroemia, tiny parasites"
131
+ awardtrophytournament,95,95,"Grand Slam, U S A, Awards"
132
+ politicalparty,95,95,"Page Content, Conservative Republicans, Rival Fatah Movement"
133
+ politicaloffice,95,95,"Co Chair, Bill, United States Presidency"
134
+ hallwayitem,93,0,"brand, foundation, fall"
135
+ model,92,92,"Dan Rather, Gabriel Garc A M Rquez, Dior"
136
+ shoppingmall,91,91,"Circus Circus, Real Place, Shares"
137
+ terroristorganization,87,87,"Chins, Palestinian Leadership, People S Liberation Army"
138
+ hospital,86,86,"Memphis, Toronto General, Memorial Sloan Kettering"
139
+ economicsector,86,0,"motorcycle, computer, home"
140
+ braintissue,86,86,"Genitourinary System, Experiment, Assessment"
141
+ color,84,84,"Pink, Shade, Green"
142
+ weatherphenomenon,83,83,"Earthquake, Clouds, Threat"
143
+ bedroomitem,82,0,"sale, air conditioning, action"
144
+ reptile,81,0,"arrow, short eared owl, wading birds"
145
+ chef,80,80,"John Gatto, Len Deighton, Jeffrey Gettleman"
146
+ currency,79,79,"American, Sierra Leone, Lloyds"
147
+ invertebrate,79,0,"infusoria, sea life, egrets"
148
+ amphibian,78,0,"albino fire salamander, salamanders of alabama, larval amphibians"
149
+ monument,77,77,"Close, Various Times, Canada Aviation Museum"
150
+ charactertrait,77,77,"Multitude, Office 2, Hoyas"
151
+ street,76,76,"Middlesex Turnpike, Capitol Street, Important Step"
152
+ visualizableobject,72,72,"Desktop Computer, Member, Telescope"
153
+ restaurant,69,69,"Duck And Waffle, Oblix, The Range Steakhouse"
154
+ wine,69,0,"burkina faso, oakville, perugia"
155
+ nongovorganization,69,69,"Fee, Communications Committee, Moderate Fatah Party"
156
+ buildingmaterial,68,68,"Iron, Drainage, Fibreglass"
157
+ videogame,66,66,"Breath Of Fire 4, Charlie And The Chocolate Factory, Vessel"
158
+ dateliteral,65,0,"1911, 1938, 2010"
159
+ nerve,65,65,"Nerve Roots, Brain Function, Damage"
160
+ architect,65,0,"robert t coles, hubert robert, max bond"
161
+ monarch,59,59,"Peter Kropotkin, All Star, Pertinax"
162
+ religion,57,57,"Firestone Park, Developments, Roman Catholic"
163
+ videogamesystem,56,56,"Xboxone, Blackberry, System Maintenance"
164
+ condiment,56,0,"mushroom sauce, lemon sauce, wasabi"
165
+ trainstation,54,54,"Ideal Base, Needs, Sacramento Rt"
166
+ mldataset,54,54,"William, Hdv, Hq"
167
+ year,53,53,"1874, 90, Start"
168
+ personsouthamerica,52,52,"Diesel, Gas, Iesus"
169
+ beach,52,52,"Pine Valley, Daufuskie Island, Pensacola Beach"
170
+ kitchenitem,52,0,"public water, copy, fridge freezer"
171
+ astronaut,51,51,"Helen Sharman, Exploration, Guion Blufordand"
172
+ muscle,50,50,"Dorsal Root Ganglion, Fatigue, Pectoralis Minor"
173
+ crimeorcharge,50,0,"gun crimes, policy, abuse"
174
+ parlourgame,50,50,"1 2 Weeks, Water, 12 Hours"
175
+ ethnicgroup,50,50,"Burundi, Turkish, Realtime"
176
+ zoo,49,49,"Birmingham Zoo, Ellen Trout Zoo, Aquarium Of The Americas"
177
+ nonprofitorganization,49,49,"Data, Charity Navigator, South Central Los Angeles"
178
+ farm,49,49,"Woodbury Farm, Farms, Cross Roads"
179
+ consumerelectronicitem,47,47,"Gba Sp, Muse, Wii Games"
180
+ tool,47,47,"Studs, Companies, Schedule Software"
181
+ agent,47,0,"metaweb, french, dreyfus funds"
182
+ mollusk,46,0,"domesticated animals, survey, sea anemones"
183
+ port,46,46,"Southampton, Port Clinton, Andalucia"
184
+ meat,46,0,"ham, whole chicken, ground chicken"
185
+ mlauthor,46,46,"Carlos Brito, Web Search, Roy Williams"
186
+ crustacean,45,0,"gulf, function, white"
187
+ musicfestival,45,45,"Annual Event, Wolf Creek Inn State Heritage Site, Newport Music Hall"
188
+ tableitem,43,0,"skills, dinner, laptop"
189
+ planet,42,42,"Reasons, 10 0, Heaven"
190
+ conference,41,41,"Filesystem, Tocs, Trsm"
191
+ arachnid,40,0,"oklahoma, middle, distance charges"
192
+ protein,40,40,"Proteases, Vitamin D, Effect"
193
+ tradeunion,40,0,"streets, program, legend"
194
+ skyscraper,40,40,"Property Types, Landmark Skyscraper, Citigroup Center"
195
+ nondiseasecondition,39,39,"Urinary Tract Infections, Symptom, Development Corporation"
196
+ visualartform,36,36,"Materials, Contemporary Art, Friends"
197
+ grain,35,0,"hrs, oil, pole beans"
198
+ visualartmovement,34,34,"Cubism, Palm Springs, United States"
199
+ fruit,34,0,"flax seed, black cherries, pineapple"
200
+ wallitem,33,33,"Description, East, Output"
201
+ candy,33,0,"gathering, peanut butter and jelly fudge, starbucks coffee"
202
+ bone,32,0,"two, ligament, chemotherapy"
203
+ highway,30,30,"Gas, Crestline, Disability"
204
+ election,30,30,"Republican, The National, Users"
205
+ mlsoftware,30,0,"mallet, p g, favorite social bookmarking websites comments"
206
+ event,29,29,"Pearl Harbour, Events Click, System Improvements"
207
+ nonneginteger,28,28,"90, Person Time, Great Time"
208
+ placeofworship,28,28,"Sleepy Hollow Presbyterian Church, Banteay Srei, Law Society"
209
+ boardgame,27,27,"Life, Computer, Anagrams"
210
+ visualizableattribute,27,27,"Dark Green Foliage, Northern States, Colour"
211
+ politicsbill,26,26,"Pennsylvania Law, States Constitution, Activesync"
212
+ householditem,26,26,"Brockton House Inn Bed, Home Office, Size Beds"
213
+ creditunion,25,25,"Good Deal, Arizona, Missouri"
214
+ perceptionaction,25,25,"Prior Approval, Mom, Waterloo"
215
+ creativework,24,24,"Uncle Silas, Print The Application, Future U S"
216
+ automobileengine,24,24,"Ford Ranger, Ford Five Hundred, Publishing"
217
+ mlalgorithm,22,22,"2 0, Murcia, 8 25"
218
+ race,22,22,"Toulouse, Dates, Morbihan"
219
+ mlarea,22,22,"Analyses, Roles, Transactions"
220
+ personalcareitem,22,22,"Lancaster, Body Parts, Hairdryer"
221
+ nut,22,0,"horses, hazelnuts, heath"
222
+ researchproject,21,21,"Wonders, Jersey Research, Consultants"
223
+ sociopolitical,21,21,"Welcome Thing, Memoirs Of A Revolutionist, Good Idea"
224
+ vein,21,21,"Section, Pituitary Gland, Macula"
225
+ archaea,20,20,"Haloarcula Strains, Mesophile Ferroplasma Acidarmanus, Acidophilic Archaea"
226
+ bridge,20,20,"Tower Bridge, Lincoln Tunnel, Life Sciences Institute"
227
+ cheese,19,19,"Close Up, Boursin Cheese, Ricotta Cheese"
228
+ legume,19,0,"close, wide screen, color television"
229
+ mountainrange,17,17,"England Mountains, Smith River, Point Hope"
230
+ physicalcharacteristic,17,17,"Possibility, Scientific Advisory, Hurst"
231
+ sportsevent,16,16,"Sarthe, Series Championship, Premier"
232
+ medicalprocedure,16,16,"Cpr, The Breast, Follow Up Study"
233
+ cardgame,16,16,"Hearts, Vegas Casino, Numbers"
234
+ militaryconflict,15,15,"Eight Years, United States Territory, War"
235
+ mlmetric,15,15,"7, 13 5, 9 11"
236
+ celltype,14,14,"Liver, Capacity, State"
237
+ petroleumrefiningcompany,14,14,"Arcelormittal, Valero, Exxon Mobil Corp"
238
+ mlconference,13,13,"Kingman, Change, Coordination"
239
+ traditionalgame,13,13,"Betfair, 3 4, 2 1 2"
240
+ meetingeventtitle,12,12,"Holey Grail The Mechanism Of Transport Through The Nuclear Pore Of Cells, Brain Maps To Mechanisms Neural Circuit Molecular Architecture, Rsa In The Real World"
241
+ convention,12,12,"Space, Excaliber, Technorati"
242
+ meetingeventtype,11,11,"Vasc Sminar, Special Sdi Seminar, Scs Author Presentation Booksigning"
243
+ judge,11,11,"The California, Great Judge, Personnel"
244
+ trail,11,11,"Carroll, Campground, Burlington"
245
+ cognitiveactions,10,10,"Learning, Crisis, Three Hours"
246
+ mediatype,10,10,"Singles, Clicks, Irrigation"
247
+ month,10,0,"november, january, december"
248
+ coffeedrink,10,0,"espresso, mocha, chocolate raspberry"
249
+ zipcode,10,10,"33701, 80202, 20505"
250
+ physicsterm,10,10,"15 Miles, 19 9 Percent, Gravity"
251
+ perceptionevent,9,9,"Improvements, Macromedia Flash, Beep"
252
+ cave,8,0,"will, w d, anyone"
253
+ lymphnode,8,8,"Lymph Nodes, Enzymes, Nodes"
254
+ filmfestival,7,7,"Igoogle, Beginning, Venice"
255
+ fungus,7,0,"compound, enrichment, mushrooms"
256
+ officeitem,7,7,"Oracle Corporation White Paper, Capacities, York Page"
257
+ continent,7,7,"South America, Africa, Antarctica"
258
+ olympics,6,6,"CBS Television Network, Palestinian National Authority, Mayor"
259
+ url,6,6,"Links Links, Numerous Links, Google Link"
260
+ game,6,6,"Designee The Vice, Team Plays, Aspects"
261
+ flooritem,5,5,"Size Sleeper Sofa, Size Sofa Bed, Water Quality"
262
+ televisionnetwork,5,5,"Cw, Upn, CNN PBS"
263
+ mediacompany,4,4,"Vibrant Media, Demand Media, Media Public"
264
+ dayofweek,4,4,"Monday, Tuesday, Wednesday"
265
+ time,4,4,"4 00 P M Contact, 9 00 Pm, 7 Click"
266
+ caf_,3,3,"Starbucks Coffee Company, Sbux, Tim Hortons"
267
+ item,3,3,"Dishes, Laptop, Laundry Detergent"
268
+ militaryeventtype,3,3,"Clashes, Attacks, Regimes"
269
+ victim,3,3,"Resources Students, Technology Students, Health Sciences Students"
270
+ geolocatablething,3,3,"Buildings, Iquique, Cars"
271
+ grandprix,2,2,"Bernie Ecclestone, Renault"
272
+ politicsgroup,2,2,"Description, Moveon Org"
273
+ gamescore,2,2,"4 5, 2 3"
274
+ refineryproduct,2,2,"East Coast Ports, Coolant"
275
+ recipe,1,1,Rice
276
+ virus,1,1,Safe Drinking Water
277
+ humanagent,1,1,Monsanto Co S G D Searle Division
278
+ species,1,1,Marine Flora
279
+ personbylocation,1,1,Dan Garton
280
+ SUM,68518,53887,