1 AND TFIDF Web DFIWF Wikipedia Web Web AND 5. Wikipedia AND 6. Wikipedia Web Ma [4] Ma URL AND Tian [8] Tian Tian Web Cimiano [3] [

Size: px
Start display at page:

Download "1 AND TFIDF Web DFIWF Wikipedia Web Web 2. 3. 4. AND 5. Wikipedia AND 6. Wikipedia Web 7. 8. 2. Ma [4] Ma URL AND Tian [8] Tian Tian Web Cimiano [3] ["

Transcription

1 DEIM Forum 2015 B Web Web AND AND Web 1. Twitter Facebook SNS Web Web Web Web [5] Bollegala [2] Web Web 1 Google Microsoft Bing Cimiano [3] Web Web Web Web Web Web Web 1 4,730, , Web Satoh [7]

2 1 AND TFIDF Web DFIWF Wikipedia Web Web AND 5. Wikipedia AND 6. Wikipedia Web Ma [4] Ma URL AND Tian [8] Tian Tian Web Cimiano [3] [5] AND Bollegala [2] AND SVM Satoh [7] Uyar [9] Satoh Web Uyar Google Yahoo Microsoft Satoh Uyar Microsoft Bing Search API

3 Histogram of Famousness 1 Frequency log( ) Famousness Famousness Hitcount.logarithm Bing Search API MeCab [1] Web Web 20 AND 4. 1 TFIDF TFIDF(term frequency / inverse document frequency) [6] AND TFIDF TFIDF C c i D i w T F w,i D i w D C i d i DF w,i w DF w,i IDF w,i IDF w,i = log( Di DF w,i ) (1) T F w,i IDF w,i T F IDF w,i TFIDF w,i = TF w,i IDF w,i (2) T F IDF w,i c i C c i TFIDF

4 log( ) AND 2 TFIDF AND AND 13,300, Web 4. 2 DFIWF 4. 1 TFIDF AND DFIWF(document frequency / inverse web frequency) DFIWF DFIWF DFIWF Web C c i d i D = {d 1, d 2,... d n } w D DF w w W F w IW F w 1 IWF w = log( ) (3) W F w DF IW F w DFIWF w = DF w IWF w (4) DF IW F w D Web w w Web DFIWF AND AND 3 DFIWF DF WF DFIWF DF DFIWF TFIDF DFIWF 3 3 AND 5. AND 4. TFIDF DFIWF TFIDF DFIWF

5 AND Web 4. TFIDF Web Web AND AND Web AND AND T c i t j T AND h i,tj famousness i W = {w 1, w 2,, w n} famousness i = w 1 h i,t1 + w 2 h i,t2 + + w n h i,tn n (5) = (w j h i,tj ) j=1 (5) AND W W AND Leave-one-out 499 AND W W 1 AND AND 5 w i AND 0 4. Satoh [7] Web Web 6. 1 Web Web Web Web Web

6 Web Web Category c W = {w 0, w 1,..., w n} c C w i f c,i Category c = {f c,0, f c,1,..., f c,n} AND t X c Y t c AND X Y b t,c b t,c = X Y X + Y (6) t c Category c t c b t,c V t w i f t,i V t = {f t,0, f t,1,..., f t,n} c Category c t c b t,c V t t Celebrity t t C t Celebrity t = c C t (b t,c Category c) + V t (7) 7 Celebrity t Celebrity t Web 7 Web Web Web Web Web 7 Web Web Web Web Web p P age p V t w i Web p f p,i Page p = {f p,0, f p,1,..., f p,n} (8) 8 Web p P age p 7 t Celebrity t cos sim t,p sim t,p = Celebrityt Pagep Celebrity t Page p (9) Web p sim t,p Web Occurrence t Occurrence t Occurrence t = P p simt,p P (10) Occurrence t t Web Web Occurrence t t Web WebAffinity t WebAffinity t = p 1 Occurrence t + p 2 (11) p 1 p 2 Web t NewsHook t Web Wikipedia Wikipedia Wikipedia t Wikipedia 1 WikiAccess t Wikimedia WikiEdit t Wikipedia WikiAccess t WikiEdit t t NewsHook t NewsHook t = p 3 WikiAccess t +p 4 WikiEdit t +p 5 (12) 11 p 1, p 2 p 3, p 4, p 5

7 Web 4 t Famousness t HitCount t WebAffinity t NewsHook t AccumulateDuration t 4 Infobox HitCount t =Famousness t WebAffinity t NewsHook t AccumulateDuration t (14) 6. 4 Web Web Wikipedia 4 Wikipedia infobox infobox t days t AccumulateDuration t AccumulateDuration t = p 6 days t + p 7 (13) p 6 p t Web WebAffinity t NewsHook t AccumulateDuration t Web Web Web Web Web 14 Famousness t Famousness t = HitCount t WebAffinity t NewsHook t AccumulateDuration t (15) 15 WebAffinity t NewsHook t AccumulateDuration t p 1 p DFIWF 15 HitCount t DFIWF DFIWF AND 2 ( ) (DFIWF) 12 Wikipedia

8 5 Result of estimation ( ) (DFIWF) Correct Correct Estimate Estimate 5 ( ) 6 (DFIWF) 13 days t t p 1 p 7 Leave-one-Out DFIWF 6 5 DFIWF Web JSPS [1] Mecab. doc/index.html. [2] Danushka Bollegala, Yutaka Matsuo, and Mitsuru Ishizuka. Measuring semantic similarity between words using web search engines. www, 7: , [3] Philipp Cimiano, Siegfried Handschuh, and Steffen Staab. Towards the self-annotating web. In Proceedings of the 13th international conference on World Wide Web, pages ACM, [4] Qiang Ma and Masatoshi Yoshikawa. Ranking people based on metadata analysis of search results. In Sven Hartmann, Xiaofang Zhou, and Markus Kirchberg, editors, Web Information Systems Engineering - WISE 2008 Workshops, volume 5176 of Lecture Notes in Computer Science, pages Springer Berlin Heidelberg, [5] Yutaka Matsuo, Hironori Tomobe, and Takuichi Nishimura. Robust estimation of google counts for social network extraction. In AAAI, volume 7, pages , [6] GERARD SALTON. Developments in automatic text retrieval. Science, 253(5023): , [7] Koh Satoh and Hayato Yamana. Hit count reliability: how much can we trust hit counts? Web Technologies and Applications, pages , [8] Tian Tian, Soon Ae Chun, and James Geller. A prediction model for web search hit counts using word frequencies. Journal of Information Science, page , [9] Ahmet Uyar. Investigation of the accuracy of search engine hit counts. Journal of Information Science, 35(4): , Web

FIT2014( 第 13 回情報科学技術フォーラム ) RD-002 Web SNS Yuanyuan Wang Gouki Yasui Yuji Hosokawa Yukiko Kawai Toyokazu Akiyama Kazutoshi Sumiya 1. Twitter 1 Facebo

FIT2014( 第 13 回情報科学技術フォーラム ) RD-002 Web SNS Yuanyuan Wang Gouki Yasui Yuji Hosokawa Yukiko Kawai Toyokazu Akiyama Kazutoshi Sumiya 1. Twitter 1 Facebo RD-002 Web SNS Yuanyuan Wang Gouki Yasui Yuji Hosokawa Yukiko Kawai Toyokazu Akiyama Kazutoshi Sumiya 1. Twitter 1 Facebook 2 SNS SNS SNS Twitter SNS [1] SNS [2] Twitter Web Web Web Web SNS Web Web 2 Web

More information

Wikipedia 2 Wikipedia Web Wikipedia 2. Web [6] [11] [8] 2 SVM Bollegala [1] 5-gram URL URL 2-gram [6] [11] SVM 3 SVM [8] Bollegala [1] SVM [7] [9] [6]

Wikipedia 2 Wikipedia Web Wikipedia 2. Web [6] [11] [8] 2 SVM Bollegala [1] 5-gram URL URL 2-gram [6] [11] SVM 3 SVM [8] Bollegala [1] SVM [7] [9] [6] DEIM Forum 2012 F3-5 305 8550 1-2 305 8550 1-2 E-mail: {yamaguchi,satoh}@ce.slis.tsukuba.ac.jp, sat@slis.tsukuba.ac.jp Wikipedia SVM Abstract A study of Retrieval in Microblogging based on Person s Aliases

More information

2 3, 4, 5 6 2. [1] [2] [3]., [4], () [3], [5]. Mel Frequency Cepstral Coefficients (MFCC) [9] Logan [4] MFCC MFCC Flexer [10] Bogdanov2010 [3] [14],,,

2 3, 4, 5 6 2. [1] [2] [3]., [4], () [3], [5]. Mel Frequency Cepstral Coefficients (MFCC) [9] Logan [4] MFCC MFCC Flexer [10] Bogdanov2010 [3] [14],,, DEIM Forum 2016 E1-4 525-8577 1 1-1 E-mail: is0111rs@ed.ritsumei.ac.jp, oku@fc.ritsumei.ac.jp, kawagoe@is.ritsumei.ac.jp 373 1.,, itunes Store 1, Web,., 4,300., [1], [2] [3],,, [4], ( ) [3], [5].,,.,,,,

More information

IPSJ SIG Technical Report Vol.2010-SLDM-144 No.50 Vol.2010-EMB-16 No.50 Vol.2010-MBL-53 No.50 Vol.2010-UBI-25 No /3/27 Twitter IME Twitte

IPSJ SIG Technical Report Vol.2010-SLDM-144 No.50 Vol.2010-EMB-16 No.50 Vol.2010-MBL-53 No.50 Vol.2010-UBI-25 No /3/27 Twitter IME Twitte Twitter 1 1 1 IME Twitter 2009 12 15 2010 2 1 13590 4.83% 8.16% 2 3 Web 10 45% Relational Analysis between User Context and Input Word on Twitter Yutaka Arakawa, 1 Shigeaki Tagashira 1 and Akira Fukuda

More information

Trial for Value Quantification from Exceptional Utterances 37-066593 1 5 1.1.................................. 5 1.2................................ 8 2 9 2.1.............................. 9 2.1.1.........................

More information

. Yahoo! 1!goo 2 QA..... QA Web Web 2 3 4 5 6 7 8 2. [1]Web Web Yin [2] Web Web Web. [3] Web Wikipedia 1 2

. Yahoo! 1!goo 2 QA..... QA Web Web 2 3 4 5 6 7 8 2. [1]Web Web Yin [2] Web Web Web. [3] Web Wikipedia 1  2 DEIM Forum 211 F6-3 Web 35 855 1 2 35 855 1 2 11 843 2 1 2 E-mail: s913153@klis.tsukuba.ac.jp, {yohei,satoh}@slis.tsukuba.ac.jp, kando@nii.ac.jp QA Web Web Web QA Diversified-query Generating System Using

More information

DEIM Forum 2012 E Web Extracting Modification of Objec

DEIM Forum 2012 E Web Extracting Modification of Objec DEIM Forum 2012 E4-2 670 0092 1 1 12 E-mail: nd11g028@stshse.u-hyogo.ac.jp, {dkitayama,sumiya}@shse.u-hyogo.ac.jp Web Extracting Modification of Objects for Supporting Map Browsing Junki MATSUO, Daisuke

More information

,,, Twitter,,, ( ), 2. [1],,, ( ),,.,, Sungho Jeon [2], Twitter 4 URL, SVM,, , , URL F., SVM,, 4 SVM, F,.,,,,, [3], 1 [2] Step Entered

,,, Twitter,,, ( ), 2. [1],,, ( ),,.,, Sungho Jeon [2], Twitter 4 URL, SVM,, , , URL F., SVM,, 4 SVM, F,.,,,,, [3], 1 [2] Step Entered DEIM Forum 2016 C5-1 182-8585 1-5-1 E-mail: saitoh-ryoh@uec.ac.jp, terada.minoru@uec.ac.jp Twitter,, Twitter,,, Bag of Words, Latent Semantic Indexing,.,,,, Twitter,, Twitter,, 1. SNS, SNS Twitter 1,,,

More information

DEIM Forum 2010 A3-3 Web Web Web Web Web. Web Abstract Web-page R

DEIM Forum 2010 A3-3 Web Web Web Web Web. Web Abstract Web-page R DEIM Forum 2010 A3-3 Web Web 305 8550 1 2 305 8550 1 2 E-mail: s0813167@u.tsukuba.ac.jp, satoh@slis.tsukuba.ac.jp Web Web Web. Web Abstract Web-page Recommendation System based on the Keyword transitions

More information

卒論タイトル

卒論タイトル 1 Web, [ ] [ ] [ ] [ ] [ ],.,,.,,., Web, Web 3. Web., 3,, IDF. 2 1 4 1.1... 4 1.2... 4 1.3... 4 1.4... 5 1.5... 5 2 6 2.1 Web UI[2]... 6 2.1.1... 6 2.1.2... 7 2.2 [3]... 7 2.2.1... 7 2.2.2... 7 2.3 Web

More information

1 3 1.1................................. 3 1.2................................... 4 1.2.1................... 4 1.2.2..................... 4 1.2.3.....

1 3 1.1................................. 3 1.2................................... 4 1.2.1................... 4 1.2.2..................... 4 1.2.3..... 2012 STUDIES ON RANKING DOCUMENTS WITH QUERY-INTENT SENSITIVITY 11R3129 Shota HATAKENAKA 1 3 1.1................................. 3 1.2................................... 4 1.2.1................... 4 1.2.2.....................

More information

1 Broder Navigational URL URL Informational Web Transactional Web Web Web 2 Broder [16] SearchLife Broder [16] Daniel [17] Broder

1 Broder Navigational URL URL Informational Web Transactional Web Web Web 2 Broder [16] SearchLife Broder [16] Daniel [17] Broder DEIM Forum 2010 B9-4 432 8011 3 5 1 432 8011 3 5 1 E-mail: gs08062@s.inf.shizuoka.ac.jp, {yokoyama,fukuta,ishikawa}@inf.shizuoka.ac.jp Web Web. Evaluation of a Multiple Viewpoints Clustering Search Engine

More information

DEIM Forum 2009 C8-4 QA NTT QA QA QA 2 QA Abstract Questions Recomme

DEIM Forum 2009 C8-4 QA NTT QA QA QA 2 QA Abstract Questions Recomme DEIM Forum 2009 C8-4 QA NTT 239 0847 1 1 E-mail: {kabutoya.yutaka,kawashima.harumi,fujimura.ko}@lab.ntt.co.jp QA QA QA 2 QA Abstract Questions Recommendation Based on Evolution Patterns of a QA Community

More information

DEIM Forum 2014 B Twitter Twitter Twitter 2006 Twitter 201

DEIM Forum 2014 B Twitter Twitter Twitter 2006 Twitter 201 DEIM Forum 2014 B2-4 305 8550 1 2 305 8550 1 2 E-mail: {yamaguchi,yamahei,satoh}@ce.slis.tsukuba.ac.jp Twitter Twitter 2 1 1. Twitter 2006 Twitter 2012 5 [1]Twitter RT RT Twitter Twitter RT Twitter 2 1

More information

main.dvi

main.dvi DEIM Forum 2015 D3-1 305-8573 1-1-1 305-8573 1-1-1 ( ) 151-0051 5-13-18 101-8430 2-1-2.com,,,, Market Share Estimation based on Statistics of Search Engine Suggests Takakazu IMADA,IchiroMORIYA, Yusuke

More information

SERPWatcher SERPWatcher SERP Watcher SERP Watcher,

SERPWatcher SERPWatcher SERP Watcher SERP Watcher, SERPWatcher 112-8610 2-1-1 112-8610 2-1-1 229-8558 5-10-1 E-mail: nakabe@db.is.ocha.ac.jp, chiemi@is.ocha.ac.jp SERPWatcher SERP Watcher SERP Watcher, SERP Analysis of transition of ranking in SERP Watcher

More information

12_24.dvi

12_24.dvi Vol. 48 No. 12 Dec. 2007 1, 2 3 3, 4 1, 2 1, 2, 3 3 Web Web Web Web Web Web tfidf tfidf Web Effectiveness of Social Tagging Based on Marking Yuki Matsuoka, 1, 2 Ryuuki Sakamoto, 3 Sadanori Ito, 3, 4 Ikki

More information

1 Web,.,, Web..,, Web.,,,.,,,., CGI.,, Web, Web.,,. PC,,.

1 Web,.,, Web..,, Web.,,,.,,,., CGI.,, Web, Web.,,. PC,,. Web 1 Web,.,, Web..,, Web.,,,.,,,., CGI.,, Web, Web.,,. PC,,. 2 1 6 1.1............................................... 6 1.2.............................................. 6 1.3...............................................

More information

,, WIX. 3. Web Index 3. 1 WIX WIX XML URL, 1., keyword, URL target., WIX, header,, WIX. 1 entry keyword 1 target 1 keyword target., entry, 1 1. WIX [2

,, WIX. 3. Web Index 3. 1 WIX WIX XML URL, 1., keyword, URL target., WIX, header,, WIX. 1 entry keyword 1 target 1 keyword target., entry, 1 1. WIX [2 DEIM Forum 2013 B10-4 Web Index 223-8522 3-14-1 E-mail: haseshun@db.ics.keio.ac.jp, toyama@ics.keio.ac.jp, URL WIX, Web Web Index(WIX). WIX, WIX.,,. Web Index, Web, Web,, Related Contents Recommendation

More information

IPSJ SIG Technical Report Vol.2009-DBS-149 No /11/ Bow-tie SCC Inter Keyword Navigation based on Degree-constrained Co-Occurrence Graph

IPSJ SIG Technical Report Vol.2009-DBS-149 No /11/ Bow-tie SCC Inter Keyword Navigation based on Degree-constrained Co-Occurrence Graph 1 2 1 Bow-tie SCC Inter Keyword Navigation based on Degree-constrained Co-Occurrence Graph Satoshi Shimada, 1 Tomohiro Fukuhara 2 and Tetsuji Satoh 1 We had proposed a navigation method that generates

More information

TF-IDF TDF-IDF TDF-IDF Extracting Impression of Sightseeing Spots from Blogs for Supporting Selection of Spots to Visit in Travel Sat

TF-IDF TDF-IDF TDF-IDF Extracting Impression of Sightseeing Spots from Blogs for Supporting Selection of Spots to Visit in Travel Sat 1 1 2 1. TF-IDF TDF-IDF TDF-IDF. 3 18 6 Extracting Impression of Sightseeing Spots from Blogs for Supporting Selection of Spots to Visit in Travel Satoshi Date, 1 Teruaki Kitasuka, 1 Tsuyoshi Itokawa 2

More information

Mining Social Network of Conference Participants from the Web

Mining Social Network of  Conference Participants from the Web Social Network Mining Social network Semantic Web, KM, Our lives are enormously influenced by relations to others. SNS Mixi, myspace, LiveJournal, Yahoo!360 FOAF WebBlog Web mining Social network mining

More information

thesis.dvi

thesis.dvi 16 2 2 i ii TF IDF an analysis of a lecture s structure based on the similarity between the slides used at the lecture Kenji MIKI Abstract In recent years, research of automatic shooting systems is done

More information

Web Web Web Twitter Web Web 2 Web Web Web Web URL Web Web 2 Web Twitter Developers Streaming API 1 2 Google Place API vervion 3 1 lm 1

Web Web Web Twitter Web Web 2 Web Web Web Web URL Web Web 2 Web Twitter Developers Streaming API 1 2 Google Place API vervion 3 1 lm 1 DEIM Forum 2016 F1-6 603-8047 755-8611 2-16-1 E-mail: {i1458085,kawai}@cc.kyoto-su.ac.jp, {g1244758,g1344270,akiyama}@cse.kyoto-su.ac.jp, y.wang@yamaguchi-u.ac.jp Web Web 1. Twitter 1 Forsquare 2 SNS Web

More information

DEIM Forum 2010 A Web Abstract Classification Method for Revie

DEIM Forum 2010 A Web Abstract Classification Method for Revie DEIM Forum 2010 A2-2 305 8550 1 2 305 8550 1 2 E-mail: s0813158@u.tsukuba.ac.jp, satoh@slis.tsukuba.ac.jp Web Abstract Classification Method for Reviews using Degree of Mentioning each Viewpoint Tomoya

More information

untitled

untitled 580 26 5 SP-G 2011 AI An Automatic Question Generation Method for a Local Councilor Search System Yasutomo KIMURA Hideyuki SHIBUKI Keiichi TAKAMARU Hokuto Ototake Tetsuro KOBAYASHI Tatsunori MORI Otaru

More information

2 : Open Clip Art Library [4] 2 3 4 5 6 2. 2 2. 1 Microsoft Office PowerPoint Web PowerPoint 2 Yahoo! Web [5] SlideShare 2. 1. 1 Yahoo! Web Yahoo! Web

2 : Open Clip Art Library [4] 2 3 4 5 6 2. 2 2. 1 Microsoft Office PowerPoint Web PowerPoint 2 Yahoo! Web [5] SlideShare 2. 1. 1 Yahoo! Web Yahoo! Web DEWS2008 E4-4 606-8501 E-mail: {hsato,oyama,tanaka}@dl.kuis.kyoto-u.ac.jp.. Supporting the Selection of Images Based on Referential Semantics from Surrounding Information of the Image in Presentation Files

More information

Web Hashtag Hashtag Twitter Hashtag Twitter Hashtag Hashtag Hashtag Twitter Hashtag Twitter Hashtag contexthashtag contexthashtag Hashtag contexthasht

Web Hashtag Hashtag Twitter Hashtag Twitter Hashtag Hashtag Hashtag Twitter Hashtag Twitter Hashtag contexthashtag contexthashtag Hashtag contexthasht DEIM Forum 2011 F5-4 contexthashtag Twitter 525 8577 1 1 1 525 8577 1 1 1 E-mail: kaieda@coms.ics.ritsumei.ac.jp, huang@fc.ritsumei.ac.jp, kawagoe@is.ritsumei.ac.jp contexthashtag Twitter Twitter Twitter

More information

Wikipedia YahooQA MAD 4)5) MAD Web 6) 3. YAMAHA 7) 8) 2 3 4 5 6 2. Vocaloid2 2006 1 PV 2009 1 1100 200 YouTube 1 minato minato ussy 3D MAD F EDis ussy

Wikipedia YahooQA MAD 4)5) MAD Web 6) 3. YAMAHA 7) 8) 2 3 4 5 6 2. Vocaloid2 2006 1 PV 2009 1 1100 200 YouTube 1 minato minato ussy 3D MAD F EDis ussy 1, 2 3 1, 2 Web Fischer Social Creativity 1) Social Creativity CG Network Analysis of an Emergent Massively Collaborative Creation Community Masahiro Hamasaki, 1, 2 Hideaki Takeda 3 and Takuichi Nishimura

More information

22 Google Trends Estimation of Stock Dealing Timing using Google Trends

22 Google Trends Estimation of Stock Dealing Timing using Google Trends 22 Google Trends Estimation of Stock Dealing Timing using Google Trends 1135064 3 1 Google Trends Google Trends Google Google Google Trends Google Trends 2006 Google Google Trend i Abstract Estimation

More information

IT i

IT i 27 The automatic extract of know-how search tag using a thesaurus 1160374 2016 2 26 IT i Abstract The automatic extract of know-how search tag using a thesaurus In recent years, a number of organizational

More information

‰gficŒõ/’ÓŠ¹

‰gficŒõ/’ÓŠ¹ The relationship between creativity of Haiku and idea search space YOSHIDA Yasushi This research examined the relationship between experts' ranking of creative Haiku (a Japanese character poem including

More information

Microsoft Word - toyoshima-deim2011.doc

Microsoft Word - toyoshima-deim2011.doc DEIM Forum 2011 E9-4 252-0882 5322 252-0882 5322 E-mail: t09651yt, sashiori, kiyoki @sfc.keio.ac.jp CBIR A Meaning Recognition System for Sign-Logo by Color-Shape-Based Similarity Computations for Images

More information

IPSJ SIG Technical Report Vol.2011-DBS-153 No /11/3 Wikipedia Wikipedia Wikipedia Extracting Difference Information from Multilingual Wiki

IPSJ SIG Technical Report Vol.2011-DBS-153 No /11/3 Wikipedia Wikipedia Wikipedia Extracting Difference Information from Multilingual Wiki Wikipedia 1 2 3 Wikipedia Wikipedia Extracting Difference Information from Multilingual Wikipedia Yuya Fujiwara, 1 Yu Suzuki 2 and Akiyo Nadamoto 3 There are multilingual articles on the Wikipedia. The

More information

DEIM Forum 2013 B6-3 MAP Web MAP Implementation and Ev

DEIM Forum 2013 B6-3 MAP Web MAP Implementation and Ev DEIM Forum 2013 B6-3 MAP 815 8540 4-9-1 815 8540 4-9-1 E-mail: 2ds11094e@s.kyushu-u.ac.jp, ushiama@design.kyushu-u.ac.jp Web MAP Implementation and Evaluation of A Browsing Interface for A Novel Using

More information

main.dvi

main.dvi DEIM Forum 2018 J7-3 305-8573 1-1-1 305-8573 1-1-1 305-8573 1-1-1 () 151-0053 1-3-15 6F URL SVM Identifying Know-How Sites basedonatopicmodelandclassifierlearning Jiaqi LI,ChenZHAO, Youchao LIN, Ding YI,ShutoKAWABATA,

More information

2. Twitter Twitter 2.1 Twitter Twitter( ) Twitter Twitter ( 1 ) RT ReTweet RT ReTweet RT ( 2 ) URL Twitter Twitter 140 URL URL URL 140 URL URL

2. Twitter Twitter 2.1 Twitter Twitter( ) Twitter Twitter ( 1 ) RT ReTweet RT ReTweet RT ( 2 ) URL Twitter Twitter 140 URL URL URL 140 URL URL 1. Twitter 1 2 3 3 3 Twitter Twitter ( ) Twitter (trendspotter) Twitter 5277 24 trendspotter TRENDSPOTTER DETECTION SYSTEM FOR TWITTER Wataru Shirakihara, 1 Tetsuya Oishi, 2 Ryuzo Hasegawa, 3 Hiroshi Hujita

More information

IPSJ SIG Technical Report Vol.2009-DPS-141 No.20 Vol.2009-GN-73 No.20 Vol.2009-EIP-46 No /11/27 1. MIERUKEN 1 2 MIERUKEN MIERUKEN MIERUKEN: Spe

IPSJ SIG Technical Report Vol.2009-DPS-141 No.20 Vol.2009-GN-73 No.20 Vol.2009-EIP-46 No /11/27 1. MIERUKEN 1 2 MIERUKEN MIERUKEN MIERUKEN: Spe 1. MIERUKEN 1 2 MIERUKEN MIERUKEN MIERUKEN: Speech Visualization System Based on Augmented Reality Yuichiro Nagano 1 and Takashi Yoshino 2 As the spread of the Augmented Reality(AR) technology and service,

More information

2reN-A14.dvi

2reN-A14.dvi 340 30 1 SP2-N 2015 Onomatoperori : Ranking Cooking Recipes by using Onomatopoeias which Express their Tastes and Textures Chiemi Watanabe Satoshi Nakamura Graduate School of Systems and Information Engineering,

More information

<> <name> </name> <body> <></> <> <title> </title> <item> </item> <item> 11 </item> </>... </body> </> 1 XML Web XML HTML 1 name item 2 item item HTML

<> <name> </name> <body> <></> <> <title> </title> <item> </item> <item> 11 </item> </>... </body> </> 1 XML Web XML HTML 1 name item 2 item item HTML DEWS2008 C6-4 XML 606-8501 E-mail: yyonei@db.soc.i.kyoto-u.ac.jp, {iwaihara,yoshikawa}@i.kyoto-u.ac.jp XML XML XML, Abstract Person Retrieval on XML Documents by Coreference that Uses Structural Features

More information

独立行政法人情報通信研究機構 Development of the Information Analysis System WISDOM KIDAWARA Yutaka NICT Knowledge Clustered Group researched and developed the infor

独立行政法人情報通信研究機構 Development of the Information Analysis System WISDOM KIDAWARA Yutaka NICT Knowledge Clustered Group researched and developed the infor 独立行政法人情報通信研究機構 KIDAWARA Yutaka NICT Knowledge Clustered Group researched and developed the information analysis system WISDOM as a research result of the second medium-term plan. WISDOM has functions that

More information

[7] [10] Web Web RDF Resource Description Framework subjectpredicate object Web Web Web Web Web 2 Web 3 4 5 6 2. Web MUC(Message Understanding Confere

[7] [10] Web Web RDF Resource Description Framework subjectpredicate object Web Web Web Web Web 2 Web 3 4 5 6 2. Web MUC(Message Understanding Confere THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. Web 113 8656 7 3 1 113 8656 7 3 1 E-mail: {tjstkm,jmori,ishizuka}@mi.ci.i.u-tokyo.ac.jp Web Web Web Web

More information

Honda 3) Fujii 4) 5) Agrawala 6) Osaragi 7) Grabler 8) Web Web c 2010 Information Processing Society of Japan

Honda 3) Fujii 4) 5) Agrawala 6) Osaragi 7) Grabler 8) Web Web c 2010 Information Processing Society of Japan 1 1 1 1 2 Geographical Feature Extraction for Retrieval of Modified Maps Junki Matsuo, 1 Daisuke Kitayama, 1 Ryong Lee 1 and Kazutoshi Sumiya 1 Digital maps available on the Web are widely used for obtaining

More information

Introduction to Information and Communication Technology (a)

Introduction to Information and Communication Technology (a) Introduction to Information and Communication Technology (a) 5 th week: 1.4 Transmission, exchange and evaluation of information Kazumasa Yamamoto Dept. Computer Science & Engineering Introduction to ICT(a)

More information

1 1 tf-idf tf-idf i

1 1 tf-idf tf-idf i 14 A Method of Article Retrieval Utilizing Characteristics in Newspaper Articles 1055104 2003 1 31 1 1 tf-idf tf-idf i Abstract A Method of Article Retrieval Utilizing Characteristics in Newspaper Articles

More information

Web PDF [7, 8] 1 1 [9, 10] OCR [9] HITS [10] 2. 3 [11] IDF TF-IDF [12] PageRank,, PageRank TF-IDF k-means PageRank Web ios 1 imac mac

Web PDF [7, 8] 1 1 [9, 10] OCR [9] HITS [10] 2. 3 [11] IDF TF-IDF [12] PageRank,, PageRank TF-IDF k-means PageRank Web ios 1 imac mac DEIM Forum 2018 E3-5 700 8530 3-1-1 101 8430 2-1-2 E-mail: {tanijiri, ohta}@de.cs.okayama-u.ac.jp, {takasu, adachi}@nii.ac.jp ipad Web ibooks ibooks 1. OCR [1 3] PDF PDF 1 2 Web Web PDF ipad Web ipad 2

More information

Danushka Bollegala 7-3-1 Keigo WATANABE Danushka BOLLEGALA Yutaka MATSUO and Mitsuru ISHIZUKA Graduate School of Information Science and Technology, T

Danushka Bollegala 7-3-1 Keigo WATANABE Danushka BOLLEGALA Yutaka MATSUO and Mitsuru ISHIZUKA Graduate School of Information Science and Technology, T Automatic Extraction of Related Terms using Web Search Engines 1 Danushka Bollegala 7-3-1 Keigo WATANABE Danushka BOLLEGALA Yutaka MATSUO and Mitsuru ISHIZUKA Graduate School of Information Science and

More information

Twitter‡Ì”À‰µ…c…C†[…g‡ðŠŸŠp‡µ‡½…^…C…•…›…C…fi‘ã‡Ì…l…^…o…„‘îŁñ„�™m

Twitter‡Ì”À‰µ…c…C†[…g‡ðŠŸŠp‡µ‡½…^…C…•…›…C…fi‘ã‡Ì…l…^…o…„‘îŁñ„�™m 27 Twitter 1431050 2016 3 14 1 Twitter,,.,.,., Twitter,.,,.,,. URL,,,. BoW(Bag of Words), LSI(Latent Semantic Indexing)., URL,,,,., Accuracy, AUC(Area Under the Curve), Precision, Recall, F,. URL,,,.,

More information

1034 IME Web API Web API 1 IME Fig. 1 Suitable situations for context-aware IME. IME IME IME IME 1 GPS Web API Web API Web API Web )

1034 IME Web API Web API 1 IME Fig. 1 Suitable situations for context-aware IME. IME IME IME IME 1 GPS Web API Web API Web API Web ) Vol. 52 No. 3 1033 1044 (Mar. 2011) IME 1 2 1 1 IME Web PC Android Dynamic Dictionary Generation Method for Context-aware Input Method Editor Yutaka Arakawa, 1 Shinji Suematsu, 2 Shigeaki Tagashira 1 and

More information

2013.10.22 Facebook twitter mixi GREE Facebook twitter mixi GREE Facebook Facebook Facebook SNS 201 1 8 Facebook Facebook Facebook Facebook 1,960 7 2012 400 Facebook SNS mixi Google Facebook Facebook

More information

1 Web DTN DTN 2. 2 DTN DTN Epidemic [5] Spray and Wait [6] DTN Android Twitter [7] 2 2 DTN 10km 50m % %Epidemic 99% 13.4% 10km DTN [8] 2

1 Web DTN DTN 2. 2 DTN DTN Epidemic [5] Spray and Wait [6] DTN Android Twitter [7] 2 2 DTN 10km 50m % %Epidemic 99% 13.4% 10km DTN [8] 2 DEIM Forum 2014 E7-1 Web DTN 112 8610 2-1-1 UCLA Computer Science Department 3803 Boelter Hall, Los Angeles, CA 90095-1596, USA E-mail: yuka@ogl.is.ocha.ac.jp, mineo@cs.ucla.edu, oguchi@computer.org Web

More information

Vol. 9 No. 5 Oct. 2002 (?,?) 2000 6 5 6 2 3 6 4 5 2 A B C D 132

Vol. 9 No. 5 Oct. 2002 (?,?) 2000 6 5 6 2 3 6 4 5 2 A B C D 132 2000 6 5 6 :, Supporting Conference Program Production Using Natural Language Processing Technologies Hiromi itoh Ozaku Masao Utiyama Masaki Murata Kiyotaka Uchimoto and Hitoshi Isahara We applied natural

More information

27 YouTube YouTube UGC User Generated Content CDN Content Delivery Networks LRU Least Recently Used UGC YouTube CGM Consumer Generated Media CGM CGM U

27 YouTube YouTube UGC User Generated Content CDN Content Delivery Networks LRU Least Recently Used UGC YouTube CGM Consumer Generated Media CGM CGM U YouTube 2016 2 16 27 YouTube YouTube UGC User Generated Content CDN Content Delivery Networks LRU Least Recently Used UGC YouTube CGM Consumer Generated Media CGM CGM UGC UGC YouTube k-means YouTube YouTube

More information

Microsoft Word - SNSで繋がる人間関係.doc

Microsoft Word - SNSで繋がる人間関係.doc SNS SNS ID SNS ixi Twitter Facebook SNS SNS SNS SNS SNS SNS Mixi Mixi Mixi SNS SNS SNS Mixi 2009 9 30 1,792 SNS 52.2% 47.8% 20 24 33.8% 25 29 28.4% 30 34 17.6% Wikipedia SNS Mixi Mixi SNS Mixi SNS Twitter

More information

”‰−ofiI…R…fi…e…L…X…g‡ðŠp‡¢‡½„�“õ„‰›Ê‡Ì™ñ”¦

”‰−ofiI…R…fi…e…L…X…g‡ðŠp‡¢‡½„�“õ„‰›Ê‡Ì™ñ”¦ 1 1 5 1.1........................................... 5 1.2.................................. 6 1.2.1.............. 6 1.2.2........................... 7 1.3........................................... 7

More information

DEIM Forum 2009 E

DEIM Forum 2009 E DEIM Forum 2009 E5-3 464-8601 1 606-8501 464 8601 1 E-mail: lifushi@arch.itc.nagoya-u.ac.jp, mayumi@mm.media.kyoto-u.ac.jp, {hirano,kajita,mase}@itc.nagoya-u.ac.jp Abstract Study on a Recipe Recommendation

More information

http://www.casej.org/ No.01, 2001 4 1 5 2 2001 7 3 8 3.1....................................... 8 3.2........................................... 8 3.3......................................... 8 3.4.........................

More information

(2008) JUMAN *1 (, 2000) google MeCab *2 KH coder TinyTextMiner KNP(, 2000) google cabocha(, 2001) JUMAN MeCab *1 *2 h

(2008) JUMAN *1 (, 2000) google MeCab *2 KH coder TinyTextMiner KNP(, 2000) google cabocha(, 2001) JUMAN MeCab *1  *2 h The Society for Economic Studies The University of Kitakyushu Working Paper Series No. 2011-12 (accepted in March 30, 2012) () (2009b) 19 (2003) 1980 PC 1990 (, 2009) (2001) (2004) KH coder (2009) TinyTextMiner

More information

キャッチーブランディングで稼ぐ

キャッチーブランディングで稼ぐ !1 1. 2. Google 3.SEO SEO!2 Google 4. 5. Google!3 .!4 !5 DVD!6 !7 Facebook Twitter SNS http://liginc.co.jp/199207!8 2. etc Google SEO!9 https://www.youtube.com/watch? v=hs9ze2y5wzk!10 !11 !12 !13 XMind

More information

untitled

untitled DEIM Forum 2019 B3-3 305 8573 1-1-1 305 8573 1-1-1 ( ) 151-0053 1-3-15 6F word2vec, An Interface for Browsing Topics of Know-How Sites Shuto KAWABATA, Ohkawa YOUHEI,WenbinNIU,ChenZHAO, Takehito UTSURO,and

More information

Haiku Generation Based on Motif Images Using Deep Learning Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura Scho

Haiku Generation Based on Motif Images Using Deep Learning Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura Scho Haiku Generation Based on Motif Images Using Deep Learning 1 2 2 2 Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura 2 1 1 School of Engineering Hokkaido University 2 2 Graduate

More information

2 2.1 SNS web Facebook Google+ SNS web SNS web HITS ANT(Auction Network Trust) web [4] SNS WEB PageRank HITS HITS web authorities, hubs Pagerank web S

2 2.1 SNS web Facebook Google+ SNS web SNS web HITS ANT(Auction Network Trust) web [4] SNS WEB PageRank HITS HITS web authorities, hubs Pagerank web S SNS Evaluation and Development reputation network for SNS user evaluation using realistic distance 1 3 1,2 Takanobu Otsuka 1 Takuya Yoshimura 3 Takayuki Ito 1,2 1 1 Center for Green Computing, Nagoya Institute

More information

DEIM Forum 2015 F8-4 Twitter Twitter 1. SNS

DEIM Forum 2015 F8-4 Twitter Twitter 1. SNS DEIM Forum 2015 F8-4 Twitter 432 8011 3-5-1 432 8011 3-5-1 E-mail: cs11032@s.inf.shizuoka.ac.jp, {yokoyama,fyamada}@inf.shizuoka.ac.jp Twitter 1. SNS SNS SNS Twitter 1 Twitter SNS facebook 2 mixi 3 Twitter

More information

_314I01BM浅谷2.indd

_314I01BM浅谷2.indd 587 ネットワークの表現学習 1 1 1 1 Deep Learning [1] Google [2] Deep Learning [3] [4] 2014 Deepwalk [5] 1 2 [6] [7] [8] 1 2 1 word2vec[9] word2vec 1 http://www.ai-gakkai.or.jp/my-bookmark_vol31-no4 588 31 4 2016

More information

2 21,238 35 2 2 Twitter 3 4 5 6 2. 2.1 SNS 2.2 2. 1 [8] [5] [7] 2. 2 SNS SNS 2 2. 2. 1 Cheng [2] Twitter [6] 2. 2. 2 Backstrom [1] Facebook 3 Jurgens

2 21,238 35 2 2 Twitter 3 4 5 6 2. 2.1 SNS 2.2 2. 1 [8] [5] [7] 2. 2 SNS SNS 2 2. 2. 1 Cheng [2] Twitter [6] 2. 2. 2 Backstrom [1] Facebook 3 Jurgens DEIM Forum 2016 B4-3 地域ユーザに着目した口コミツイート収集手法の提案 長島 里奈 関 洋平 圭 猪 筑波大学 情報学群 知識情報 図書館学類 305 8550 茨城県つくば市春日 1 2 筑波大学 図書館情報メディア系 305 8550 茨城県つくば市春日 1 2 つくば市役所 305 8555 茨城県つくば市研究学園 1 1 1 E-mail: s1211530@u.tsukuba.ac.jp,

More information

¥ì¥·¥Ô¤Î¸À¸ì½èÍý¤Î¸½¾õ

¥ì¥·¥Ô¤Î¸À¸ì½èÍý¤Î¸½¾õ 2013 8 18 Table of Contents = + 1. 2. 3. 4. 5. etc. 1. ( + + ( )) 2. :,,,,,, (MUC 1 ) 3. 4. (subj: person, i-obj: org. ) 1 Message Understanding Conference ( ) UGC 2 ( ) : : 2 User-Generated Content [

More information

1 4 4 [3] SNS 5 SNS , ,000 [2] c 2013 Information Processing Society of Japan

1 4 4 [3] SNS 5 SNS , ,000 [2] c 2013 Information Processing Society of Japan SNS 1,a) 2 3 3 2012 3 30, 2012 10 10 SNS SNS Development of Firefighting Knowledge Succession Support SNS in Tokyo Fire Department Koutarou Ohno 1,a) Yuki Ogawa 2 Hirohiko Suwa 3 Toshizumi Ohta 3 Received:

More information

教師情報を必要としないWebページ群のコンテンツ自動抽出ツールの提案

教師情報を必要としないWebページ群のコンテンツ自動抽出ツールの提案 DEIM Forum 2009 A8-4 Web 305-8573 1-1-1 305-8573 1-1-1 E-mail: m.yoshida@mibel.cs.tsukuba.ac.jp, myama@cs.tsukuba.ac.jp CMS Web Web Web Web Web Web Web Web,,, HTML, Web, Web, Primary Content Extraction

More information

3.5 検索で上位に表示させるタイトル付けの奥義

3.5 検索で上位に表示させるタイトル付けの奥義 3.5 1 2 SEO 3 5 15 18 21 32 3.5 URL AdobeReader ( ) 1 / 33 3.5 2 / 33 3.5 SEO 3.3 SEO Search Engine Optimization 3 / 33 3.5 4 / 33 3.5 5 / 33 3.5 Yahoo! 6 / 33 3.5 CM 7 / 33 3.5 8 / 33 3.5 9 / 33 3.5 10

More information

Vol. 28 No. 2 Apr. 2011 173 1. 1 Web Twitter/Facebook UI 4 1. 2. 3. 4. Twitter Web Twitter/Facebook e.g., Web Web UI 1 2 SNS 1, 2 2

Vol. 28 No. 2 Apr. 2011 173 1. 1 Web Twitter/Facebook UI 4 1. 2. 3. 4. Twitter Web Twitter/Facebook e.g., Web Web UI 1 2 SNS 1, 2 2 172 SNS Web Web As social web sites such as blog and SNS(Social Network System) became popular, many people have communicated with their friends on the Web. Meanwhile, several problems of social web sites

More information

wki_shuronn.pdf

wki_shuronn.pdf No. 161 Sentiment Extraction from Live Tweets 2014 3 Twitter Twitter Summary Recently, microblogs such as Twitter become popular, and we can tweet about our own daily life easily. The user who tweets during

More information

Microsoft PowerPoint - takeda-panel.ppt

Microsoft PowerPoint - takeda-panel.ppt パネル討論情報爆発時代における理論と実際 武田英明 国立情報学研究所東京大学人工物工学研究センター takeda@nii.ac.jp 自己紹介 元々は非情報系 ( でも実質人工知能のような研究でした ) 情報系としては人工知能分野 知識エージェントによる協調的問題解決 ( 西田先生と ) Webからの知識獲得 ( オントロジー ) セマンティックWeb??? 1 自己紹介 自分のパネルでの立場 理論研究者

More information

IPSJ SIG Technical Report Vol.2014-HCI-157 No.26 Vol.2014-GN-91 No.26 Vol.2014-EC-31 No /3/15 1,a) 2 3 Web (SERP) ( ) Web (VP) SERP VP VP SERP

IPSJ SIG Technical Report Vol.2014-HCI-157 No.26 Vol.2014-GN-91 No.26 Vol.2014-EC-31 No /3/15 1,a) 2 3 Web (SERP) ( ) Web (VP) SERP VP VP SERP 1,a) 2 3 Web (SERP) ( ) Web (VP) SERP VP VP SERP VP Web 1. Web Web Web Web Google SERP SERP 1 1 2-1-1, Hodokubo, Hino, Tokyo 191 8506, Japan 2 4-12-3, Higash-Shinagawa, Shinagawa, Tokyo 140 0002, Japan

More information

..,,,, , ( ) 3.,., 3.,., 500, 233.,, 3,,.,, i

..,,,, , ( ) 3.,., 3.,., 500, 233.,, 3,,.,, i 25 Feature Selection for Prediction of Stock Price Time Series 1140357 2014 2 28 ..,,,,. 2013 1 1 12 31, ( ) 3.,., 3.,., 500, 233.,, 3,,.,, i Abstract Feature Selection for Prediction of Stock Price Time

More information

ルール&マナー集_社内版)_修正版.PDF

ルール&マナー集_社内版)_修正版.PDF WWW(World Wide Web) Web 12 WWW ID 2 1 2 3 4 WWW World Wide Web 5 5 A B 11 http://www.enc.or.jp/enc/code/rule/main.html 12 3 ... 2 1... 5 1.1... 5 1.2... 5 1.3... 6 1.4... 7 2... 9 2.1... 9 2.2 ID... 10

More information

A Japanese Word Dependency Corpus ÆüËܸì¤Îñ¸ì·¸¤ê¼õ¤±¥³¡¼¥Ñ¥¹

A Japanese Word Dependency Corpus   ÆüËܸì¤Îñ¸ì·¸¤ê¼õ¤±¥³¡¼¥Ñ¥¹ A Japanese Word Dependency Corpus 2015 3 18 Special thanks to NTT CS, 1 /27 Bunsetsu? What is it? ( ) Cf. CoNLL Multilingual Dependency Parsing [Buchholz+ 2006] (, Penn Treebank [Marcus 93]) 2 /27 1. 2.

More information

IPSJ SIG Technical Report Vol.2011-MUS-91 No /7/ , 3 1 Design and Implementation on a System for Learning Songs by Presenting Musical St

IPSJ SIG Technical Report Vol.2011-MUS-91 No /7/ , 3 1 Design and Implementation on a System for Learning Songs by Presenting Musical St 1 2 1, 3 1 Design and Implementation on a System for Learning Songs by Presenting Musical Structures based on Phrase Similarity Yuma Ito, 1 Yoshinari Takegawa, 2 Tsutomu Terada 1, 3 and Masahiko Tsukamoto

More information

Vol.20, No.1, 2018 Castillo [10] Yang [11] Sina Weibo 3 Castillo [10] Twitter 4 Twitter [12] Twitter ) 2 Twitter [13] 3. Twitter Twitter 3

Vol.20, No.1, 2018 Castillo [10] Yang [11] Sina Weibo 3 Castillo [10] Twitter 4 Twitter [12] Twitter ) 2 Twitter [13] 3. Twitter Twitter 3 Vol.20 No.1, 2018 1 2 3 4 Construction of Information-credibility Verification-behavior Facilitation System for Preventing False Rumors Spreading Daisuke Kakimoto 1, Mai Miyabe 2, Eiji Aramaki 3 and Takashi

More information

Vol. 46 No. SIG 13(TOD 27) ) ),4) 4) 1 2 5) 2 Cutting Scatter/Gather 6),7) Fractionation 6) Leuski 8)

Vol. 46 No. SIG 13(TOD 27) ) ),4) 4) 1 2 5) 2 Cutting Scatter/Gather 6),7) Fractionation 6) Leuski 8) Vol. 46 No. SIG 13(TOD 27) Sep. 2005 94 95 IREX A Label-based Navigation Method Using Informatively Named Entities Hiroyuki Toda, Hidekazu Nakawatase and Ryoji Kataoka Due to the growth of the Internet,

More information

1. [1, 2, 3] (PDF ) [4] API API [5] ( ) PDF Web Web Annotate[6] Digital Library for Earth System Education(DLESE)[7] Web PDF Text, Link, FreeTe

1. [1, 2, 3] (PDF ) [4] API API [5] ( ) PDF Web Web Annotate[6] Digital Library for Earth System Education(DLESE)[7] Web PDF Text, Link, FreeTe aoyama@info.suzuka-ct.ac.jp yamaji@nii.ac.jp Sharing system of annotation for paper publication Toshihiro AOYAMA Department of Electronic and Information Engineering, Suzuka National College of Technology

More information

Web Web Web Web Web, i

Web Web Web Web Web, i 22 Web Research of a Web search support system based on individual sensitivity 1135117 2011 2 14 Web Web Web Web Web, i Abstract Research of a Web search support system based on individual sensitivity

More information

滋賀県研究者情報システムのテキストマイニングによる性能改善について

滋賀県研究者情報システムのテキストマイニングによる性能改善について I 1 Web 2 2003 348 Tani 2004 5 2008 348 Shinichi Taniguchi / recall precision 1 2 CHHY HUY 100 2014 Spring / No.399 Fig.1 2011 2013 25,000 1 3 II 20% 2002 TLO 3 Ohmura 2003 TLO TLO COC 4 Fig.1 Web 検 索

More information

[1] [3]. SQL SELECT GENERATE< media >< T F E > GENERATE. < media > HTML PDF < T F E > Target Form Expression ( ), 3.. (,). : Name, Tel name tel

[1] [3]. SQL SELECT GENERATE< media >< T F E > GENERATE. < media > HTML PDF < T F E > Target Form Expression ( ), 3.. (,). : Name, Tel name tel DEIM Forum 2011 C7-5 SuperSQL 223 8522 3 14 1 E-mail: tomonari@db.ics.keio.ac.jp, toyama@ics.keio.ac.jp SuperSQL, SQL SELECT GENERATE SQL., SuperSQL HTML,.,. SuperSQL, HTML, Equivalent Transformation on

More information

TA3-4 31st Fuzzy System Symposium (Chofu, September 2-4, 2015) Interactive Recommendation System LeonardoKen Orihara, 1 Tomonori Hashiyama, 1

TA3-4 31st Fuzzy System Symposium (Chofu, September 2-4, 2015) Interactive Recommendation System LeonardoKen Orihara, 1 Tomonori Hashiyama, 1 Interactive Recommendation System 1 1 1 1 LeonardoKen Orihara, 1 Tomonori Hashiyama, 1 Shun ichi Tano 1 1 Graduate School of Information Systems, The University of Electro-Communications Abstract: The

More information

[1] HITS EigenRumor Web PageRank 情報の要求 投稿者推薦システム 投稿者の重要度推定 ( 本研究 ) の引用回数から推定 投稿者のネットワークから推定 個人的な興味を考慮した部分 1 投稿者のランキング Web EigenRumor Kri

[1] HITS EigenRumor Web PageRank 情報の要求 投稿者推薦システム 投稿者の重要度推定 ( 本研究 ) の引用回数から推定 投稿者のネットワークから推定 個人的な興味を考慮した部分 1 投稿者のランキング Web EigenRumor Kri DEIM Forum 21 C3-4 66-81 66-81 E-mail: spzuka@db.soc.i.kyoto-u.ac.jp, {ysuzuki,yoshikawa}@i.kyoto-u.ac.jp A calculation method of blogger s importance using influences to others in micro-blogs Kazuki YOSHIMOTO,

More information

2009 2

2009 2 2009 2 350603022 ( ) Wii iii 1 1 2 7 2.1............................ 7 2.1.1...................... 7 2.1.2........... 8 2.1.3......................... 10 2.2....................... 11 2.2.1.................

More information

IPSJ-TOD

IPSJ-TOD Vol. 3 No. 2 91 101 (June 2010) 1 1 1 2 1 TSC2 Automatic Evaluation of Text Summaries by Using Paraphrase Kazuho Hirahara, 1 Hidetsugu Nanba, 1 Toshiyuki Takezawa 1 and Manabu Okumura 2 The evaluation

More information

DEIM Forum 2019 C3-5 tweet

DEIM Forum 2019 C3-5 tweet DEIM Forum 2019 C3-5 tweet 163 8677 1 24 2 163 8677 1 24 2 163 8677 1 24 2 E-mail: c515029@ns.kogakuin.ac.jp, cm17051@ns.kogakuin.ac.jp, aki@cc.kogakuin.ac.jp Twitter tweet tweet tweet BoW Doc2vec SVM

More information

Web 1 q q 2 1 2 Step1) Twitter Step2) (w i, w j ) S(w i, w j ) Step3) q 2 2 2.1 I Twitter MeCab[6] URL http:// @ 2.2 (w i, w j ) S(w i, w j ) I w i w

Web 1 q q 2 1 2 Step1) Twitter Step2) (w i, w j ) S(w i, w j ) Step3) q 2 2 2.1 I Twitter MeCab[6] URL http:// @ 2.2 (w i, w j ) S(w i, w j ) I w i w ARG WI2 No.6, 2015 a b b 565-0871 2-1 a) yoshitake@nanase.comm.eng.osaka-u.ac.jp b) {naoko, babaguchi}@comm.eng.osaka-u.ac.jp 1 Citizen Sensor [1] Twitter 140 Twitter Sakaki [2] [3] Massoudi [4] [5] Copyright

More information

06sugiyama.dvi

06sugiyama.dvi Web Web Web Web Personal Name Disambiguation in Web Search Results Using a Semi-Supervised Clustering Approach Kazunari Sugiyama and Manabu Okumura Personal names are often submitted to search engines

More information

1 Fogg Fogg Behavior Model [1] information cascade [2] TPO [3] Fig. 2 Target area of this paper. 1 Fig. 1 Fogg b

1 Fogg Fogg Behavior Model [1] information cascade [2] TPO [3] Fig. 2 Target area of this paper. 1 Fig. 1 Fogg b 1,a) 1 1 1 2014 9 20, 2015 1 5 TPO Extracting Purpose-for-Action to Enhance Local Information Service Noriko Yokoyama 1,a) Kaname Funakoshi 1 Hiroyuki Toda 1 Yoshimasa Koike 1 Received: September 20, 2014,

More information

4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q

4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q x-means 1 2 2 x-means, x-means k-means Bayesian Information Criterion BIC Watershed x-means Moving Object Extraction Using the Number of Clusters Determined by X-means Clustering Naoki Kubo, 1 Kousuke

More information

( : A8TB2163)

( : A8TB2163) 2011 2012 3 26 ( : A8TB2163) ( A B [1] A B A B B i 1 1 2 3 2.1... 3 2.1.1... 3 2.1.2... 4 2.2... 5 3 7 3.1... 7 3.2... 7 3.3 A B... 7 4 8 4.1... 8 4.1.1... 9 4.1.2... 9 4.1.3... 9 4.1.4... 10 4.2 A B...

More information

IPSJ SIG Technical Report Vol.2009-HCI-134 No /7/17 1. RDB Wiki Wiki RDB SQL Wiki Wiki RDB Wiki RDB Wiki A Wiki System Enhanced by Visibl

IPSJ SIG Technical Report Vol.2009-HCI-134 No /7/17 1. RDB Wiki Wiki RDB SQL Wiki Wiki RDB Wiki RDB Wiki A Wiki System Enhanced by Visibl 1. RDB Wiki 1 1 2 Wiki RDB SQL Wiki Wiki RDB Wiki RDB Wiki A Wiki System Enhanced by Visible RDB Operations Toshiya Okumura, 1 Minoru Terada 1 and Kazutaka Maruyama 2 Although Wiki systems can easily be

More information