SlowGuess/NAACL_2022_OCR huggingface dataset:
Table 22: Cluster Names and Word Scores - Military Vehicles
Cluster Name	Word Scores
0_flying_aircraft_helicopter_aviation	(flying, 0.02), (aircraft, 0.02), (helicopter, 0.02), (aviation, 0.01), (spitfire, 0.01), (flight, 0.01), (raf, 0.01), (jet, 0.01), (plane, 0.01), (fly, 0.01)
1_tank_abrams_army_m1	(tank, 0.07), (abrams, 0.04), (army, 0.03), (m1, 0.02), (m1a2, 0.02), (tanks, 0.01), (m1a1, 0.01), (armored, 0.01), (turret, 0.01), (armor, 0.01)
2_rafale_ india_france_iaf	(rafale, 0.08), (india, 0.04), (france, 0.03), (ief, 0.03), (mirage2000, 0.02), (jet, 0.02), (dassault, 0.02), (aircraft, 0.02), (combat, 0.01), (greece, 0.01)
-1_whathappeninginmyanmar_military_wa_jet	(whathappeninginmyanmar, 0.02), (military, 0.02), (wa, 0.02), (jet, 0.01), (aircraft, 0.01), (helicopter, 0.01), (plane, 0.01), (landed, 0.01), (flight, 0.01), (flying, 0.01)
3_crashed_jet(plane_pilot	-crashed, 0.05), (jet, 0.03), (plane, 0.03), (pilot, 0.03), (abuja, 0.02), (killed, 0.02), (nigerian, 0.02), (airport, 0.02), (aircraft, 0.02), (nige-ria, 0.02)
4_syria_iran_syrian_israel	(syria, 0.06), (iran, 0.04), (syrian, 0.04), (israel, 0.02), (turkey, 0.02), (russia, 0.02), (yemen, 0.02), (libya, 0.01), (gaza, 0.01), (iraq, 0.01)
5_typhoon_eurofighter_raf_aviation	(typhoon, 0.21), (eurofighter, 0.20), (raf, 0.03), (aviation, 0.02), (war-planeporn, 0.02), (jet, 0.01), (luftwaffe, 0.01), (aviationphotography, 0.01), (tornado, 0.01), (squadron, 0.01)
6_drone.ai_uav_drones	(drone, 0.18), (ai, 0.05), (uav, 0.04), (drones, 0.03), (unmanned, 0.02), (tech, 0.02), (hacker, 0.02), (uas, 0.02), (intelligence, 0.01), (artificial, 0.01)
7_whathappeninginmyanmar_myanmar_yangon_junta	(whathappeninginmyanmar, 0.09), (myanmar, 0.07), (yangon, 0.05), (junta, 0.04), (terrorist, 0.02), (savemyanmar, 0.02), (whathappen-inglnmyanmar, 0.02), (protester, 0.02), (coup, 0.02), (violence, 0.02)
8_tornado_raf_flying_aviation	(tornado, 0.20), (raf, 0.06), (flying, 0.04), (aviation, 0.02), (aeroplane, 0.02), (squadron, 0.01), (aircraft, 0.01), (jet, 0.01), (airtoair, 0.01), (flypast, 0.01)
9_china_taiwan_chinese_southchinasea	(china, 0.12), (taiwan, 0.12), (chinese, 0.10), (southchinasea, 0.02), (aircraft, 0.02), ( airspace, 0.02), (taiwanstrait, 0.02), (beijing, 0.02), (japan, 0.02), (luzonstrain, 0.01)
Table 22: Cluster Names and Word Scores - Military Vehicles
Cluster Name	Word Scores
10_ag600_amphibious_china-flyight	(ag600, 0.31), (amphibious, 0.29), (china, 0.23), (flight, 0.08), (zhuhai, 0.05), (avic, 0.04), (qingdao, 0.03), (aircraft, 0.03), (shandong, 0.03), (guangdong, 0.02)
11_WALLpaper_f22_aircraft_eagle	(wallpaper, 0.49), (f22, 0.07), (aircraft, 0.06), (eagle, 0.06), (wall-papers, 0.04), (falcon, 0.04), (walpapier, 0.04), (eurofighter, 0.03), (walpapers, 0.03), (backgrounds, 0.03)
12_woman_pilot-fly wwii	(woman, 0.15), (pilot, 0.08), (fly, 0.04), (wwii, 0.03), (airforce, 0.02), (pilots, 0.02), (flying, 0.02), (squadron, 0.02), (aircraft, 0.01), (flight, 0.01)
13-mar23coup_dawei_htaung_bike	(mar23coup, 0.23), (dawei, 0.23), (htaung, 0.22), (bike, 0.22), (road, 0.19), (whathappeninginmyanmar, 0.14), (death, 0.06), (dead, 0.01), (crimesagainsthumanity, 0.01), (weneedr2pinmyanmar, 0.01)
14_kenya_zimbabwe_nigeria_ghana	(kenya, 0.07), (zimbabwe, 0.07), (nigeria, 0.06), (ghana, 0.04), (bribery, 0.04), (brazil, 0.03), (harare, 0.03), (ethiopia, 0.02), (africa, 0.02), (buhari, 0.02)
15_optionstrading_stockmarket_stocks_investing	(optionstrading, 0.24), (stockmarket, 0.24), (stocks, 0.24), (investing, 0.24), (satellites, 0.23), (investment, 0.23), (stock, 0.22), (boeing, 0.15), (shares, 0.04), (aircraft, 0.02)
16_korea_korean_northkorea_southkorea	(korea, 0.21), (korean, 0.10), (northkorea, 0.06), (southkorea, 0.03), (war, 0.03), (ww3, 0.03), (nuclear, 0.02), (pyongyang, 0.02), (japan, 0.02), (seoul, 0.02)
17_south_korea_russian_airspace	(south, 0.31), (korea, 0.26), (russian, 0.19), (airspace, 0.17), (korean, 0.14), (southkorea, 0.04), (military, 0.03), (russia, 0.03), (seoul, 0.03), (aircraft, 0.03)
18_squawking_circling_mph-mile	(squawking, 0.25), (circling, 0.24), (mph, 0.08), (mile, 0.07), (creek, 0.07), (nsw, 0.06), (qld, 0.06), (county, 0.05), (marin, 0.03), (lake, 0.02)
19_f1_audi_lap_racing	(f1, 0.14), (audi, 0.05), (lap, 0.05), (racing, 0.04), (ferrari, 0.03), (vettel, 0.03), (mclaren, 0.03), (laps, 0.02), (raced, 0.02), (racer, 0.02)
20_concorde_mach_mph_raf	(concorde, 0.44), (mach, 0.25), (mph, 0.14), (raf, 0.11), (flying, 0.09), (rapidly, 0.06), (fuel, 0.06), (jet, 0.06), (supersonic, 0.05), (speed, 0.03)
21_turkish_naval_seahawk Rescue	(turkish, 0.26), (naval, 0.17), (seahawk, 0.08), (rescue, 0.05), (hawk, 0.05), (turkey, 0.04), (sea, 0.04), (anatolian, 0.04), (tactical, 0.04), (squadron, 0.04)
22 ACTIONS_feb21 coup_un_whatshappeninglnmyanmar	(actions, 0.28), (feb21coup, 0.23), (un, 0.23), (whatshappeninglnmyanmar, 0.20), (news, 0.14), (terrorism, 0.08), (whatshappening-inmyanmar, 0.01), (colombia, 0.01), (whatishappeninginmyanmar, 0.01), (coup, 0.00)
Figure 7: Cluster Hierarchy for Climate Change
Figure 8: Cluster Hierarchy for COVID-19
Figure 9: Cluster Hierarchy for Military Vehicles