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1 B3IM
2 ( )
3 10., B3IM2018, i
4 Document Level GeoLocation Toponym Resolution Mention Detection( ) Entity Resolution( ) LOC( ) FAC( ) RAIL( ) ROAD( ) ORG( ) GEN( ) FIC( ) AMB( ) BOT ii
5 Mention Detection Entity Resolution URL POPULATION MINDIST POPULATION+MINDIST iii
6 A 48 B 49 B B B B B B B B B B B B B B iv
7 Twitter Mention Detection Entity Resolution v
8 LOC( ) FAC( ) ( / / ) LOC( ) FAC( ) ( / / ) POPULATION MINDIST POPULATION+MINDIST vi
9 1 Twitter 1 Twitter Twitter Twitter GPS Middleton [1] 1%
10 (1) (1) (1) Akiba 3 2
11
12 2 Document GeoLocation Toponym Resolution Document Level GeoLocation Document Level GeoLocation Web Pyalling [2] IP Web Serdyukov [3] Flickr 3 Lieberman [4] global lexicon local lexicon Cheng [5] howdy Twitter Wing [6] [7] Roller [8] Document Level GeoLocation Toponym Resolution 2.2 Toponym Resolution Toponym Resolution toponym 3 4
![[3] Flickr 3 Lieberman [4] global lexicon local lexicon Cheng [5] howdy Twitter Wing [6]](/docs-images/51/20162404/images/page_12.jpg "[7] Roller [8] Document Level GeoLocation Toponym Resolution 2.")
13 London Smith [9] Ladra [10] Speriosu [11] Wikipedia 4 Indirect Supervision Paradesi [12] Twitter GPS Middleton [1] GPS 1% GPS Leidner [13] Leidner TR-CoNLL Crane [14] CWAR 4 5
![Twitter GPS Middleton [1] GPS 1% GPS Leidner [13]](/docs-images/51/20162404/images/page_13.jpg "Leidner TR-CoNLL Crane [14] CWAR 4 http://en.")
14 3 (2) (2) (3) (4) 0-0 (5) (6) (3) (4) (5) (6) ( ) 6
15 4 3 IREX [15] [16] Mention Detection Entity Resolution Mention Detection( ) 4.3 7
16 4.2 Entity Resolution( ) Mention Detection( ) 1 Akiba LOC( ) 8
17 1: LOC( ) FAC( ) RAIL( ) ROAD( ) 4 ORG( ) GEN( ) FIC( ) AMB( ) 950hpa FAC( ) USJ 9
18 4.3.3 RAIL( )!!!!! ROAD( ) ORG( ) GEN( ) 10
19 4.3.7 FIC( ) AMB( ) [17] 4.5 Web Mention Detection( ) Entity Resolution( )
20 2: Yahoo! : 2 1 edit 3 12
21 2: 4 ElasticSearch 6 search search
22 3: B 14
23 4: 4.8 Leidner [13] Speriosu [11] Twitter
24 5: Twitter Twitter 5 16
25 (7) Twitter (8) 2015 D (8) D (9) (10) 17
26 (9) (10) Twitter (9) (10) (10) (9) 1 1 (11) (12) (13) (11) (13) (13) (13) (12) (11) (13)
27 (14) 1900 wwwwwwwwwww wwwwwwwwwww wwwwwwwwwwww (15) ( ) (14) (15) (16) (17) (16) (17) MeCab 7 [18] BOT Twitter BOT PC Twitter Twitter (18) (19) (12/02 05:15) Partly Cloudy(12.2 ). (20)
28 BOT (18) (19) (20) 5 BOT BOT BOT BOT 5 BOT BOT BOT 5 BOT (19) (21) (22) (21) (22) (21) (22) 20
29 4.9 Twitter 2 BOT Twitter
30 , , Mention Detection Mention Detection IOB2 IOB2 22
31 B-FAC I-FAC I-FAC O O O O 2 F 3 IOB2 2 Cohen s Kappa O Cohen s Kappa LOC( ) FAC( ) ORG( ) (23) (23) Entity Resolution Entity Resolution
32 3: 2 F β=1 LOC( ) 90.16% (174/193) 96.67% (174/180) FAC( ) 84.09% ( 74/ 88) 72.55% ( 74/102) RAIL( ) % ( 9/ 9) 56.25% ( 9/ 16) ROAD( ) 66.67% ( 2/ 3) 40.00% ( 2/ 5) ORG( ) 84.75% ( 50/ 59) 81.97% ( 50/ 61) GEN( ) 50.00% ( 4/ 8) 57.14% ( 4/ 7) AMB( ) 16.67% ( 1/ 6) % ( 1/ 1) FIC( ) 0.00% ( 0/ 1) 0.00% ( 0/ 0) 0.00 Overall 86.01% (504/586) 88.11% (504/572) ,648 72, % (24) ( 70.8km) [ A:LOC/ B:FAC/ ( )] (25) ( 68.9km) [ A:FAC/ B:LOC/ ] (26) ( 8.6km) [ A:LOC/ B:FAC/ ] (24) A B 24
33 (25) A B (26) , , ORG( ) ORG( ) 25
34 4: LOC( ) FAC( ) ( / / ) LOC( ) 977 (68/8/901) FAC( ) 356 (51/19/286) RAIL( ) 61 ROAD( ) 7 ORG( ) 208 GEN( ) 32 FIC( ) 3 AMB( ) % % (27) K (28) (27) K K (28) 26
35 5: LOC( ) FAC( ) ( / / ) LOC( ) 406 (14/94/298) FAC( ) 517 (41/273/203) RAIL( ) 25 ROAD( ) 3 GEN( ) 65 FIC( ) 24 AMB( ) A 27
36 (29) (30) (31) NHK (29) (31) NHK 6.2 (32) 1 (33) 28
37 (32) (33) (33) 6.3 (34) w10 (35) (34) (35) 6.4 (36) (37) Ladra [10] (36) (37) 6.5 (38) (39) (40) 29
38 (38) (39) (40) % % 6.6 (41) (42) No /29( ) (41) Twitter (42) (42) 6.7 (43) (44) (43) (44)!! 30
39 6.8 (45) # (46) (45) (46) (45) (46) 6.9 URL (47) (47) URL 31
40 6: (1) 118(44.2%) 123(72.8%) 241(55.3%) (2) 6(2.2%) 7(4.1%) 13(3.0%) (3) 4(1.5%) 6(3.6%) 10(2.3%) (4) 140(52.4%) 2(1.2%) 142(32.6%) (5) 3(1.1%) 76(45.0%) 79(18.1%) (6) 0(0.0%) 3(1.8%) 3(0.7%) (7) 1(0.4%) 7(4.1%) 8(1.8%) (8) 1(0.4%) 1(0.6%) 2(0.5%) (9) 1(0.4%) 3(1.8%) 4(0.9%) (10) URL 0(0.0%) 1(0.6%) 1(0.2%) 7 5 Speriosu [19] POPULATION MINDIST POPULATION+MINDIST 7.1 POPULATION POPULATION 32
41 2 7.2 MINDIST MINDIST MINDIST Algorithm POPULATION+MINDIST Speriosu [19] POPULATION MINDIST POPULATION+MINDIST POPULATION POPULATION POP- ULATION MINDIST MINDIST MINDIST
42 Algorithm 1 MINDIST for do for i do overallmin for a i do totaldist 0 for j do if i j then min for b j do dist distance( a, b) if dist < min then min dist end if end for totaldist totaldist + min end if end for if totaldist < overallmin then overallmin totaldist a end if end for i end for end for 34
43 MINDIST 10, MINDIST Speriosu [19] 161km 100mile A POPULATION MINDIST POPULATION+MINDIST 7 35
44 7: POPULATION MINDIST POPULATION+MINDIST POPULATION ( ) POPULATION ( ) A 161 A 161 A MINDIST POPULATION + MINDIST POPULATION MINDIST POPULATION+MINDIST POPULATION MINDIST A
45 MINDIST POPULATION+MINDIST A 161 MINDIST MINDIST (48) (48) (vino tei) (vino tei) 37
46 Yahoo! Mention Detection Entity Resolution
47 6: Mention Detection Mention Detection Entity Resolution Mention Detection [ ] 4.3 Mention Detection 4.6 Entity Resolution
48 7 Mention Detection ElasticSearch Entity Resolution % Twitter 40
49 7: Entity Resolution Hecht [20] 66% Twitter Cheng [5] 41
50 9 3 4 Mention Detection Entity Resolution Mention Detection Entity Resolution
51 140 Wikipedia 43
52 44
53 [1] Stuart Middleton, Lee Middleton, and Stefano Modafferi. Real-time crisis mapping of natural disasters using social media [2] Alexei Pyalling, Michael Maslov, and Pavel Braslavski. Automatic geotagging of russian web sites. In Proceedings of the 15th international conference on World Wide Web, pp ACM,2006. [3] Pavel Serdyukov, Vanessa Murdock, and Roelof Van Zwol. Placing flickr photos on a map. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, pp ACM, [4] Michael D Lieberman, Hanan Samet, and Jagan Sankaranarayanan. Geotagging with local lexicons to build indexes for textually-specified spatial data. In Data Engineering (ICDE), 2010 IEEE 26th International Conference on, pp IEEE, [5] Zhiyuan Cheng, James Caverlee, and Kyumin Lee. You are where you tweet: acontent-basedapproachtogeo-locatingtwitterusers.inproceedings of the 19th ACM international conference on Information and knowledge management, pp ACM,2010. [6] Benjamin P Wing and Jason Baldridge. Simple supervised document geolocation with geodesic grids. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies- Volume 1, pp Association for Computational Linguistics, [7] Benjamin Wing and Jason Baldridge. Hierarchical discriminative classification for text-based geolocation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp , Doha, Qatar, October Association for Computational Linguistics. [8] Stephen Roller, Michael Speriosu, Sarat Rallapalli, Benjamin Wing, and Jason Baldridge. Supervised text-based geolocation using language models on an adaptive grid. In Proceedings of the 2012 Joint Conference on Empirical 45
54 Methods in Natural Language Processing and Computational Natural Language Learning, pp Association for Computational Linguistics, [9] David A Smith and Gregory Crane. Disambiguating geographic names in a historical digital library. In Research and Advanced Technology for Digital Libraries, pp Springer,2001. [10] Susana Ladra, Miguel R Luaces, Oscar Pedreira, and Diego Seco. A toponym resolution service following the ogc wps standard. In Web and Wireless Geographical Information Systems, pp Springer,2008. [11] Michael Speriosu and Jason Baldridge. Text-driven toponym resolution using indirect supervision. In ACL (1), pp ,2013. [12] Sharon Myrtle Paradesi. Geotagging tweets using their content. In FLAIRS Conference, [13] Jochen L Leidner. An evaluation dataset for the toponym resolution task. Computers, Environment and Urban Systems, Vol. 30, No. 4, pp , [14] Gregory Crane. The perseus digital library tufts.edu/hopper/. [15] Satoshi Sekine and Yoshio Eriguchi. Japanese named entity extraction evaluation: analysis of results. In Proceedings of the 18th conference on Computational linguistics-volume 2, pp Association for Computational Linguistics, [16],,.., (NL ), pp , [17] Satoshi Sekine, Kiyoshi Sudo, and Chikashi Nobata. Extended named entity hierarchy. In LREC, [18] Taku Kudo, Kaoru Yamamoto, and Yuji Matsumoto. Applying conditional random fields to japanese morphological analysis. In EMNLP, Vol.4,pp ,
55 [19] Michael Adrian Speriosu. Methods and applications of text-driven toponym resolution with indirect supervision [20] Brent Hecht, Lichan Hong, Bongwon Suh, and Ed H Chi. Tweets from justin bieber s heart: the dynamics of the location field in user profiles. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp ACM,
56 A SA 48
57 B B.0.3 (49) [ LOC/ ] [ FAC/NULL (NOTE= )] (50) [ LOC/NULL (NOTE= )] (49) FUKUROKOJI cafe (50) (NOTE) B.0.4 (51) [ LOC/NULL (NOTE= )] (52) [ FAC/NULL (NOTE= )] (51) (52) B.0.5 (53) [ LOC/NULL (NOTE= )] [ LOC/NULL (NOTE= )] (53) 49
58 B.0.6 (54) [ FAC/NULL (NOTE= )] (55) [ FAC/NULL (NOTE= )] (56) [ LOC/NULL (NOTE= )] (54) (55) (56) B.0.7 (57) [ LOC/ ] (58) [ LOC/ ] 1 11 (57) (58) B.0.8 (59) [ ORG] (60) [ FAC/NULL (NOTE= )] (59) ( ) (60) 50
59 B.0.9 (61) [ D FAC/ ] (62) [TDL FAC/ ] (61) D (62) TDL B.0.10 (63) [ FAC/ ] (63) B.0.11 (64) (65) M3 (64) (65) M3 (64) B.0.12 (66) (66) 51
60 B.0.13 (67) [ LOC/ ] (68) [ LOC/ ] (67) (68) B.0.14 (69) [ FAC/ ]2 202 (70) [ LOC/ ] [ FAC/ Akiba] (69) 202 (70) B.0.15 (71) [ FAC/ ] 3 (72) 100m [ FAC/NULL ] (71) 3 (72) 100m 52
61 B.0.16 (73) [ LOC/NULL (NOTE= )] (73) (73) 53
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36 知能と情報知能と情報 ( 日本知能情報ファジィ学会誌 ( ))Vol.29, No.6, pp.226-230(2017) 会告 Zadeh( ザデー ) 先生を偲ぶ会 のご案内 Zadeh( ) とと と 日 2018 1 20 日 ( ) 15:00 17:30(14:30 18:00 ) 2F ( ) 530-8310 1-1-35 TEL: 06-6372-5101 https://www.hankyu-hotel.com/hotel/osakashh/index.html