数学 数学 数学之美 Google 数学 Google 1 (Statistical Language Models) Google 学 - 学 Noam Chomsky 学 数学 (Claude Shannon) 数学 数学 (Fred Jelinek) IBM 学 (Sabbatical Lea
|
|
- ようじろう かむら
- 7 years ago
- Views:
Transcription
1 数学之美 * October 2, 2010 * 1
2 数学 数学 数学之美 Google 数学 Google 1 (Statistical Language Models) Google 学 - 学 Noam Chomsky 学 数学 (Claude Shannon) 数学 数学 (Fred Jelinek) IBM 学 (Sabbatical Leave) 学 S w 1, w 2,, w n S S 数学 S P(S) S P (S) P (S) = P (w 1 )P (w 2 w 1 )P (w 3 w 1, w 2 ) P (w n w 1, w 2 w n 1 ). P (w 1 ) w 1 P (w 2 w 1 ) w n wi w i 1 ( S P (S) = P (w 1 )P (w 2 w 1 )P (w 3 w 2 ) P (w i w i 1 ), ( N-1 2
3 P (w i w i 1 ) 数 数 (w i 1, w i ) w i 1 数 P (w i w i 1 ) = P (w i 1, w i )/P (w i 1 ) 数学 学 Google 美 (NIST) Google 数学 美 之 学 数学 学 Google 2 美 / / / / / 美 / / / / / 学 学 数 数 学 - 学 - 学 - - 学 90 学 数 数学 3
4 S A 1, A 2, A 3,, A k, B 1, B 2, B 3,, B m, C 1, C 2, C 3,, C n, A 1, A 2, B 1, B 2, C 1, C 2 A 1, A 2,, A k P P (A 1, A 2, A 3,, A k ) > P (B 1, B 2, B 3,, B m ), P (A 1, A 2, A 3,, A k ) > P (C 1, C 2, C 3,, C n ). Dynamic Programming Viterbi 学 学 学 学 学 学 学 学 Computational Linguistics 学 学 学 Google 之 数学 Google Critical Tokenization and its Properties 4. Chinese word segmentation without using lexicon and hand-crafted training data 4
5 3 数学 之 数学 之 s 1, s 2, s 3, o 1, o 2, o 3 o 1, o 2, o 3, s 1, s 2, s 3, 学 Hidden Markov Model o 1, o 2, o 3 s 1, s 2, s 3 数学 o 1, o 2, o 3, P (s 1, s 2, s 3, o 1, o 2, o 3, ) s 1, s 2, s 3, 数 P (o 1, o 2, o 3, s 1, s 2, s 3, ) P (s 1, s 2, s 3, ). P (o 1, o 2, o 3, s 1, s 2, s 3, ) s 1, s 2, s 3, o 1, o 2, o 3,, P (s 1, s 2, s 3, ) s 1, s 2, s 3, s 1, s 2, s 3, 数 s 1, s 2, s 3, 1. s 1, s 2, s 3, s i s i 1 2. i o i s i, P (o 1, o 2, o 3, s 1, s 2, s 3, ) = P (o 1 s 1 ) P (o 2 s 2 ) P (o 3 s 3 ) 5
6 Viterbi s1, s2, s3, 之 s 1, s 2, s 3, s 1, s 2, s 3, o 1, o 2, o 3, o 1, o 2, o 3, P (o 1, o 2, o 3, s 1, s 2, s 3, ) 学 (Acoustic Model) (Translation Model) (Correction Model) P (s 1, s 2, s 3, ) Baum 60 IBM Fred Jelinek ( ) 学 Jim and Janet Baker ( ) ( 30% 10%) Sphinx 学 学 数学 之 4? :Google 1948 (shāng)? ? 1-8?
7 bit 数, 数 数 数 log log 32 = 5, log 64 = 6 美 32 数 (p 1 log p 1 + p 2 log p p 32 log p 32 ). p 1 p 2,, p (Entropy) H 32 数学 X H(X) = x P (x) log 2 [P (x)] 数 10% 95% KB 1MB 数 redundancy) 250 数 学 7
8 5 之美 数 [ Google Page Rank ( ) 数 George Boole) 学数学 数学 之 数学 数学 1854 An Investigation of the Laws of Thought, on which are founded the Mathematical Theories of Logic and Probabilities 数学 数 1 TRUE ) 0 FALSE ) AND) (OR) NOT) AND-NOT AND AND (0), 1 0 OR OR NOT NOT 数学 数 数 数 数 数学 8
9 TRUE, 1 FALSE, 0 AND AND (NOT ) True False 数 之 数 数 1 0 数 数 之 数 数 AND 数 数 1 数 数 数 Alta Vista 学 3-5 数 之 Shards) 数学 9
10 6 (Web Crawlers) [ 数学 数学 学 数学 数 数 数 (Web Crawlers) 之 Google Trends 数学 数学 ] Traverse) 数学 Leonhard Euler 1736 Konigsberg 学 BFS) DFS) Hyperlinks) Robot) 学 (MIT) 学. Matthew Gray)
11 ( www wanderer ) (Hash Table) Google 数 (Fred Jelinek) (Perplexity) Sphinx Mutual Information) Kullback-Leibler Divergence) Bush 美 美 Kerry Kerry 11
12 美 Bush (Gale) (Church) (Yarowsky) 学 (Mitch Marcus) Kullback-Leibler Divergence 数 数 - TF/IDF) TF/IDF TF/IDF 数学 学. (Thomas Cover) (Elements of Information Theory) (Fred Jelinek) 学 学 学 学 D 学 学 A 1949 美 美 学 学 学 8 学 学 学 12
13 学 学 Roman Jakobson ( [ ] 学 (Noam Chomsky) 学 学 学 之 学 学 学 学 IBM 学 学 学 1972 IBM IBM T.G. Watson Labs 学 IBM 之 IBM Bahl Dragon (Della Pietra) BCJR (Cocke) (Raviv) IBM Google, 学 学 学 美 BCJR 数 之 IBM IBM 之 Amaden BCJR IBM IBM 学 IBM IBM 学 IBM 数 学 CLSP 学 学 CLSP CLSP 之 学 学 学 学 学 学 学 学 学 学 学 IBM, AT&T Google 13
14 学 学 学 学 学 美 学 学 Pascale 学 学 学 [ (Page Rank) 学 ] 数 数 数 Term Frequency) 14
15 数 w 1, w 2,, w N :T F 1, T F 2,, T F N TF: term frequency) :T F 1 + T F T F N 数 Google 15
16 学 学 学 AT&T 学 Mohri, Pereira Riley C AT&T 学 AT&T AT&T 学 学 Google AT&T C 学 学 AT&T 11 Google 47.. Nicolas Cage) 之 Lord of War) 47( AK47) ( 47 ( Google. (Amit Singhal) Google 47 Google Google Matt Cutts Spam) 学 学 美 数 40% Google 16
17 debug) 之 (Salton) AT&T 数 AT&T Google Google Google AT&T 学 Google Google 学 2005 学 40 美 RAID) (Randy Katz) 12 Google 数 数 TF/IDF / TF/IDF) TF/IDF TF/IDF 64,000 TF/IDF 17
18 TF/IDF ,000 数 64,000 之 学 数 a, b c A, B C A cos A = b2 + c 2 a 2 2bc b c cos A = a b a b b c X Y x 1, x 2,, x y 1, y 2,, y cos θ = x 1 y 1 + x 2 y x y x x x y y y 学学 数学 18
19 13 数 Fingerprint) URL) Google 数学之美 A7%D6%AE%C3%C0&sr=&z=&cl=3&f=8&wd=%CE%E2%BE%FC+%CA%FD%D1%A7%D6%AE% C3%C0&ct= TB GB 50% 4TB 数 数 数 /6 16 数 Fingerprint) 数 128 数 数, 数 prng) prng 之 数 数 数 ( 数 MersenneTwister,, Cookie cookie cookie MersenneTwister 数 数 csprng) MD5 SHA 数 SHA1 19
20 14 数学 [ 数学之美 数学 Google 学 学 学 之 美 之 学 20
21 8-10 学 学 数 数 学 学 学 Verrier Google 1. 数学 数 4. / 数 TF/IDF) page rank) 15 数学之美 学 美 学 (Michael Collins) 21
22 15.1 美 (Mitch Marcus) 学 学 学 (MIT) 数 数 (sentence parser) (Eric Brill) Ratnaparkhi Eisnar 数学 美 AT&T AT&T MIT MIT 学 15.2 美 (Eric Brill) 学 学 学 (transformation rule based machine learning) chang 学 美 (part of speech tagging) Google 学 学 Google Google 之 22
23 16 [ 数学 (the maximum entropy principle) ] Google 学 wang-xiao-bo ( ) 学 学 数学 (maximum entropy) AT&T 1/6 之 1/3 2/15 1/3 之 23
24 wang-xiao-bo 学 数学 Csiszar 数 数 w 3 w 1 w 2 subject P (w 3 w 1, w 2, subject) = e{λ 1(w 1,w 2,w 3 )+λ 2 (subject,w 3 )} Z(w 1, w 2, subject) 数 lambda Z 数 之 数 数 数 数 数 数 GIS(generalized iterative scaling) GIS N 数 数 3. 2 GIS Darroch Ratcliff 数学 Csiszar) Darroch Ratcliff GIS 64 GIS (Della Pietra) IBM GIS IIS improved iterative scaling 数 IBM 24
25 数学 美 学 美 学 IBM (Adwait Ratnaparkhi) 之 学 学 数学 IIS 数 (language model) 20 SUN Google IBM 学 IBM (hedge fund) - (Renaissance Technologies) 学 数学 % Berkshire Hathaway) 16 数学 数学 17 (Search Engine Anti-SPAM) (SPAM) 25
26 数 数 (page rank) Google Google Matt Cutts Google ( Google 数 学 数学 26
27 Google ( 18 学学 数 数 学 学 学 学 数学 数 Singular Value Decomposition SVD) A A = a 11 a 1j a 1N a i1 a ij a in a M1 a Mj a MN M=1,000,000 N=500,000 i j j i TF/IDF) X B Y 数 1.5 之 数 X 数 Y 27
28 之 A w 数 数 Google MapReduce Google Google Google 19 (Bayesian Networks) (Markov Chain) 之 之 之 28
29 (belief) (belief networks) 之 之 数 数 NP-complete IBM Watson (Geoffrey Zweig) 学 (Jeff Bilmes) 之 Google Google 20 学 (Mitch Marcus) 学 AT&T 学 学 LDC 学 数 数 数 (corpus) 数 学 美 学 DARPA 学 数 PennTree Bank PennTree Bank 29
30 LDC 学 数 LDC 之 学 try-and-error 学 学 学 bioinformatics ( 学 学 学 学 学 学 学 学 学 21 Bloom Filter FBI hash table Yahoo,Hotmail Gmai spamer 1.6GB googlechinablog.com/2006/08/blog-post.html 50 数学 1/8 1/4. 数 X 数 F 1, F 2,, F 8 f 1, f 2,, f 8 数 G 1 30
31 数 g 1, g 2,, g 8 Y 数 F 1, F 2,, F 8 s 1, s 2,, s 8 t 1, t 2,, t 8 Y t 1, t 2,, t 8 之 之 22 学 数学 学 学 数学 学 EBKTBP CAESAR 学 31
32 A B C E B A D F E K R P S T 数 学 美 美 AF 美 美 美 AF 美 AF 美 学 美 学 学 之 数学 Caesar 数 Ascii X= 数 数 P Q 100, N = P Q, M = (P 1) (Q 1) 2. M 数 E M E 1 数 3. 数 D E D M 1 E D mod M = 1 4. E 32
33 D N X Y X K mod N = Y D Y X D Y X Y D mod N = X N,E D, 3. E D N 数 N P Q P Q 数 P Q 50 RSA-158 数 = 数 N N=P Q 33
34 P Q 之 数?? 学?? game theory 数学 数 p 1, p 2, p 3,, p 6700 L 1, L 2, L 3,, L 6700 p 1 L 1 + p 2 L p 6700 L 6700 GBK googlechinablog.com/2006/04/4.html H = p 1 log p 1 p 6700 log p log 26 = /4.7 = /4.7= /4.7 = 1.3 学 学 34
35 之 数 2.98 数 100 http : //tools.google.com/pinyin/ 24 Google T-Mobile HTC Android 3G 学 Dynamic Programming 之 shortest path 数 数 数 数 数 数 数 35
36 数 Dynamic Programming programming 数学 -> -> -> -> -> -> Y 1, Y 2, Y 3,, Y N W 11, W 12, W 13 Y1 W 21, W 22, W 23, W 24 Y2 36
37 数学 数学 37
38 38
IBM-Mode1 Q: A: cash money It is fine today 2
8. IBM Model-1 @NICT mutiyama@nict.go.jp 1 IBM-Mode1 Q: A: cash money It is fine today 2 e f a P (f, a e) â : â = arg max a P (f, a e) â P (f, a e) 3 θ P (f e, θ) θ f d = { f, e } L(θ d) = log f,e d P
More informationgengo.dvi
4 97.52% tri-gram 92.76% 98.49% : Japanese word segmentation by Adaboost using the decision list as the weak learner Hiroyuki Shinnou In this paper, we propose the new method of Japanese word segmentation
More informationTrial 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 informationuntitled
2 Outline 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. Digital information world 2.1 2.1.1 2.1.2 2.1.3 2.2 2.2.1 2.2.2 2.2.3 2.2.4 2.2.5 2.3 2.3.1 2.3.2 2.3.3 2.1 Bit & information amount 2.1.1 2.1.2 2.1.3 2.1.4
More information1 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] [
DEIM Forum 2015 B1-5 606 8501 606 8501 E-mail: komurasaki@dl.kuis.kyoto-u.ac.jp, tajima@i.kyoto-u.ac.jp Web Web AND AND Web 1. Twitter Facebook SNS Web Web Web Web [5] Bollegala [2] Web Web 1 Google Microsoft
More information21 Pitman-Yor Pitman- Yor [7] n -gram W w n-gram G Pitman-Yor P Y (d, θ, G 0 ) (1) G P Y (d, θ, G 0 ) (1) Pitman-Yor d, θ, G 0 d 0 d 1 θ Pitman-Yor G
ol2013-nl-214 No6 1,a) 2,b) n-gram 1 M [1] (TG: Tree ubstitution Grammar) [2], [3] TG TG 1 2 a) ohno@ilabdoshishaacjp b) khatano@maildoshishaacjp [4], [5] [6] 2 Pitman-Yor 3 Pitman-Yor 1 21 Pitman-Yor
More informations t 1, 2,..., 10 s t a, b,..., k t s 1, 2,..., 10 1 a, b,..., k 1 s t ts 1 0 ( 2.25) ½ ¾ ½¼ x 1j = 1 x 2c = 1 x 3e = 1
72 2 2 2 2.24 2 s t, 2,..., 0 s t a, b,..., k t s, 2,..., 0 a, b,..., k s t 0 ts 0 ( 2.25) 2.24 2 ½ ¾ ½¼ x j = x 2c = x 3e = x 4s = x 5g = x 6i = x 7d = x 8h = x 9f = x 0k = x ta = x tb = x ts = 9 2.26
More information情報理論 第5回 情報量とエントロピー
5 () ( ) ( ) ( ) p(a) a I(a) p(a) p(a) I(a) p(a) I(a) (2) (self information) p(a) = I(a) = 0 I(a) = 0 I(a) a I(a) = log 2 p(a) = log 2 p(a) bit 2 (log 2 ) (3) I(a) 7 6 5 4 3 2 0 0.5 p(a) p(a) = /2 I(a)
More informationGrund.dvi
24 24 23 411M133 i 1 1 1.1........................................ 1 2 4 2.1...................................... 4 2.2.................................. 6 2.2.1........................... 6 2.2.2 viterbi...........................
More informationohp1.dvi
2008 1 2008.10.10 1 ( 2 ) ( ) ( ) 1 2 1.5 3 2 ( ) 50:50 Ruby ( ) Ruby http://www.ruby-lang.org/ja/ Windows Windows 3 Web Web http://lecture.ecc.u-tokyo.ac.jp/~kuno/is08/ / ( / ) / @@@ ( 3 ) @@@ :!! ( )
More informationJournal04-03&04.PMD
Japan Translation Journal No.210 Japan Translation Federation Report 1 2 3 4 Honrenso No.86 5 No.87 6 Information JTF 8 10 PR 12 News 13 14 15 JTF 16 16 104-0032 2-8-1 3F TEL 03-3555-6365 FAX 03-3552-1784
More informationA 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 informationModal Phrase MP because but 2 IP Inflection Phrase IP as long as if IP 3 VP Verb Phrase VP while before [ MP MP [ IP IP [ VP VP ]]] [ MP [ IP [ VP ]]]
30 4 2016 3 pp.195-209. 2014 N=23 (S)AdvOV (S)OAdvV 2 N=17 (S)OAdvV 2014 3, 2008 Koizumi 1993 3 MP IP VP 1 MP 2006 2002 195 Modal Phrase MP because but 2 IP Inflection Phrase IP as long as if IP 3 VP Verb
More information..,,,, , ( ) 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 informationuntitled
c 645 2 1. GM 1959 Lindsey [1] 1960 Howard [2] Howard 1 25 (Markov Decision Process) 3 3 2 3 +1=25 9 Bellman [3] 1 Bellman 1 k 980 8576 27 1 015 0055 84 4 1977 D Esopo and Lefkowitz [4] 1 (SI) Cover and
More informationNo. 3 Oct The person to the left of the stool carried the traffic-cone towards the trash-can. α α β α α β α α β α Track2 Track3 Track1 Track0 1
ACL2013 TACL 1 ACL2013 Grounded Language Learning from Video Described with Sentences (Yu and Siskind 2013) TACL Transactions of the Association for Computational Linguistics What Makes Writing Great?
More information2
2012 2012 9 7 1 2 3 von Mises, (,) algorithmic probability, Solomonoff, Hutter, MDL Vitányi 4 1900 von Mises Kolmogorov algorithmic probability, 5 6 7 Aristotle B.C. 384-322 Tyche automaton ( ) 8 Augustine
More information離散最適化基礎論 第 11回 組合せ最適化と半正定値計画法
11 okamotoy@uec.ac.jp 2019 1 25 2019 1 25 10:59 ( ) (11) 2019 1 25 1 / 38 1 (10/5) 2 (1) (10/12) 3 (2) (10/19) 4 (3) (10/26) (11/2) 5 (1) (11/9) 6 (11/16) 7 (11/23) (11/30) (12/7) ( ) (11) 2019 1 25 2
More information自然言語処理23_175
2 Sequence Alignment as a Set Partitioning Problem Masaaki Nishino,JunSuzuki, Shunji Umetani, Tsutomu Hirao and Masaaki Nagata Sequence alignment, which involves aligning elements of two given sequences,
More informationimai@eng.kagawa-u.ac.jp (phishing) URL HP web URL URL Phishing Phishing "fishing" "sophisticated" WikiPedia http://en.wikipedia.org/wiki/phishing "fishing" "phreaking" phreak http://www.atmarkit.co.jp/fsecurity/special/83phishing/phishing00.html
More information& 3 3 ' ' (., (Pixel), (Light Intensity) (Random Variable). (Joint Probability). V., V = {,,, V }. i x i x = (x, x,, x V ) T. x i i (State Variable),
.... Deeping and Expansion of Large-Scale Random Fields and Probabilistic Image Processing Kazuyuki Tanaka The mathematical frameworks of probabilistic image processing are formulated by means of Markov
More informationh23w1.dvi
24 I 24 2 8 10:00 12:30 1),. Do not open this problem booklet until the start of the examination is announced. 2) 3.. Answer the following 3 problems. Use the designated answer sheet for each problem.
More informationMicrosoft PowerPoint - …Z…O…†…fi…g…‡…f…‰‡É‡æ‡é™ñ‘oflÅ
セグメントモデルによる音声認識 NTTコミュニケーション科学基礎研究所南泰浩 セグメントモデルとは? HMM の欠点 継続時間モデルが導入されていない 状態内の観測系列の時間依存性を反映できない 改良 セグメントモデル HMM とセグメントモデルの違い y t y 1 y 2 y 3 y T P s (y t ) P a,t (y 1,y 2,y 3 y T ) s HMM a P(T a) セグメントモデル
More information2
NTT 2012 NTT Corporation. All rights reserved. 2 3 4 5 Noisy Channel f : (source), e : (target) ê = argmax e p(e f) = argmax e p(f e)p(e) 6 p( f e) (Brown+ 1990) f1 f2 f3 f4 f5 f6 f7 He is a high school
More informationt.dvi
T-1 http://adapt.cs.tsukuba.ac.jp/moodle263/course/view.php?id=7 (Keisuke.Kameyama@cs.tsukuba.ac.jp) 29 10 11 1 ( ) (a) (b) (c) (d) SVD Tikhonov 3 (e) 1: ( ) 1 Objective Output s Known system p(s) b =
More information23_33.indd
23 16 26 25 24 2 30 2 19 20 1 21 1 22 9 11 15 14 23 2 3 5 1 6 12 14 29 P.26 P.26 P.26 P.26 P.2 P.26 P.2 P.2 P.2 P.2 P.2 P.2 P.24 P.24 P.24 P.24 P.24 MAC 10. 10.6 10.5 1TB 2TB XP XP MAC 10. 10. 10.6 10.5
More informationA11 (1993,1994) 29 A12 (1994) 29 A13 Trefethen and Bau Numerical Linear Algebra (1997) 29 A14 (1999) 30 A15 (2003) 30 A16 (2004) 30 A17 (2007) 30 A18
2013 8 29y, 2016 10 29 1 2 2 Jordan 3 21 3 3 Jordan (1) 3 31 Jordan 4 32 Jordan 4 33 Jordan 6 34 Jordan 8 35 9 4 Jordan (2) 10 41 x 11 42 x 12 43 16 44 19 441 19 442 20 443 25 45 25 5 Jordan 26 A 26 A1
More informationIPSJ SIG Technical Report Vol.2010-CVIM-170 No /1/ Visual Recognition of Wire Harnesses for Automated Wiring Masaki Yoneda, 1 Ta
1 1 1 1 2 1. Visual Recognition of Wire Harnesses for Automated Wiring Masaki Yoneda, 1 Takayuki Okatani 1 and Koichiro Deguchi 1 This paper presents a method for recognizing the pose of a wire harness
More information22 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*2.5mm ”ŒŠá‡ÆfiÁ™¥‡Ì…Z†[…t…X…N…−†[…j…fi…O
I. Takeuchi, Nagoya Institute of Technology 1/38 f(x) = w 1 x 1 + w 2 x 2 +... + w d x d f(x) = α 1 K(x, x 1 ) + α 2 K(x, x 2 ) +... + α n K(x, x n ) {wj } d j=1 f {αi } n i=1 f I. Takeuchi, Nagoya Institute
More informationTD 2048 TD 1 N N 2048 N TD N N N N N N 2048 N 2048 TD 2048 TD TD TD 2048 TD 2048 minimax 2048, 2048, TD, N i
28 2048 2048 TD Computer Players Based on TD Learning for Game 2048 and Its Two-player Variant 2048 2048 TD 2048 TD 1 N N 2048 N TD N N N N N N 2048 N 2048 TD 2048 TD TD TD 2048 TD 2048 minimax 2048, 2048,
More informationNLC配布用.ppt
Semantic Web September 20, 200 IBM( ) (uramoto@jp.ibm.com) Semantic Web ( )? Semantic Web 2 What can it do? (by Jim Hendler) 3 Semantic Web W3C Director Berners-Lee Web The Semantic Web is an extension
More informationCorrected Version NICT /11/15, 1 Thursday, May 7,
Corrected Version NICT 26 2008/11/15, 1 1 Word Sketch Engine (Kilgarriff & Tugwell 01; Srdanovic, et al. 08) 2 2 3 3 ( ) I-Language Grammar is Grammar and Usage is Usage (Newmeyer 03) 4 4 (is-a ) ( ) (
More information1 J 2 tasu =: + (Tacit definition) (Explicit definition) 1.1 (&) x u&v y Fork Bond & Bond(&) 0&{ u u v v v y x y 1&{ ( p) ( q) x v&
1 J SHIMURA Masato jcd02773@nifty.ne.jp 2008 12 8 1 J 1 2 J 4 3 5 4 8 5 /de Morgan law 11 6 16 7 19 8 Reference 21 A 21 J 5 1 J J Atom ) APL J 1 J 2 tasu =: + (Tacit definition) (Explicit definition) 1.1
More information1 Abstract 2 3 n a ax 2 + bx + c = 0 (a 0) (1) ( x + b ) 2 = b2 4ac 2a 4a 2 D = b 2 4ac > 0 (1) 2 D = 0 D < 0 x + b 2a = ± b2 4ac 2a b ± b 2
1 Abstract n 1 1.1 a ax + bx + c = 0 (a 0) (1) ( x + b ) = b 4ac a 4a D = b 4ac > 0 (1) D = 0 D < 0 x + b a = ± b 4ac a b ± b 4ac a b a b ± 4ac b i a D (1) ax + bx + c D 0 () () (015 8 1 ) 1. D = b 4ac
More informationIPSJ SIG Technical Report Vol.2014-NL-219 No /12/17 1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e) 1. [23] 1(a) 1(b) [19] n-best [1] 1 N
1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e) 1. [23] 1(a) 1(b) [19] n-best [1] 1 Nara Institute of Science and Technology a) akabe.koichi.zx8@is.naist.jp b) neubig@is.naist.jp c) ssakti@is.naist.jp
More informationNatural Language Processing Series 1 WWW WWW 1. ii Foundations of Statistical NLPMIT Press 1999 2. a. b. c. 25 3. a. b. Web WWW iii 2. 3. 2009 6 v 2010 6 1. 1.1... 1 1.2... 4 1.2.1... 6 1.2.2... 12 1.2.3...
More information25 About what prevent spoofing of misusing a session information
25 About what prevent spoofing of misusing a session information 1140349 2014 2 28 Web Web [1]. [2] SAS-2(Simple And Secure password authentication protocol, ver.2)[3] SAS-2 i Abstract About what prevent
More information‰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 informationIW2002-B5 1 Internet Week ( ) 9:30 12:30 ( ) Copyright 2002 All Rights Reserved, by Seiji Kumagai ADSL FTTH 24 IP LAN
1 Internet Week 2002 20021218() 9:3012:30 () kuma@isid.co.jp ADSLFTTH 24 IP LAN LAN LAN 2 1 ? 3? 4 e-japan 20053000 20051000 2 IP»» 5 CATV DSL FTTH LAN 6 620(20029) CATV 180DSL 422FTTH 12 14 3 MP3CD CM
More informationResearch on decision making in multi-player games with imperfect information
Research on decision making in multi-player games with imperfect information 37-086521 22 2 9 UCT UCT 46 % 60000 9 % 1 1 1.1........................................ 1 1.2.....................................
More informationmatsuda.dvi
The on-line full-text database of the Minutes of the Diet: Its potentials and limitations Kenjiro Matsuda Abstract The on-line full-text database of the Minutes of the Diet offers linguists a unique resource
More information( ) (, ) arxiv: hgm OpenXM search. d n A = (a ij ). A i a i Z d, Z d. i a ij > 0. β N 0 A = N 0 a N 0 a n Z A (β; p) = Au=β,u N n 0 A
( ) (, ) arxiv: 1510.02269 hgm OpenXM search. d n A = (a ij ). A i a i Z d, Z d. i a ij > 0. β N 0 A = N 0 a 1 + + N 0 a n Z A (β; p) = Au=β,u N n 0 A-. u! = n i=1 u i!, p u = n i=1 pu i i. Z = Z A Au
More informationH1-2-3-4.indd
1 1 1 2 3 9 9 10 10 12 12 14 14 16 16 17 18 19 21 28 1 26 11 22 26 11 23 26 11 24 Web 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 4 4 8 2 2 4 20 4 4 8 4 4 8 2 2 4 4 4 32 4 4 12 4 4 4 4 4 4 4 4 8 2
More information1 Foundations of Artificial Intelligence (Overview) artificial intelligence; AI Logic Theorist 1956 Dartmouth Conference Turing test image recognition
2017 4 18 PDF aidic PDF aidic 1 15 15-1 0-a http://www.gravel.co.jp/pdfs/aidic_itemlist.htm aidic TeX PDF aidic@gravel.co.jp 1 Foundations of Artificial Intelligence (Overview) artificial intelligence;
More information23回会社説明会資料(HP用)
FFG Part FFG 09 09 1 20074 Core Core Bank Bank 170 50 32 12 68 IT 4050 4050 Core Core Value Value Part Part 2006 3 06/3 06/12 06/3 30.0% 4.5% 25.5% 26.2% 0.7% 47,500 6,300 41,200 42,200
More informationスライド 1
2010.2.18 CSS Nite in Ginza Vol.44 JIS X 8341-3 60 1 2004 vs 2010 2010 2 18 2010.2.18 CSS Nite in Ginza Vol.44 3 2004 10 Web Web JIS X 8341-3 W3C / WCAG 2010.2.18 CSS Nite in Ginza Vol.44 2010.2.18 CSS
More informationuntitled
Oracle Enterprise Repository etrust SiteMinder 10g 3 (10.3) 2008 10 Oracle Enterprise Repository etrust SiteMinder Setup and Configuration Guide, 10g Release 3 (10.3) Copyright 2007, 2008, Oracle. All
More information( : A9TB2096)
2012 2013 3 31 ( : A9TB2096) Twitter i 1 1 1.1........................................... 1 1.2........................................... 1 2 4 2.1................................ 4 2.2...............................
More informationSO(2)
TOP URL http://amonphys.web.fc2.com/ 1 12 3 12.1.................................. 3 12.2.......................... 4 12.3............................. 5 12.4 SO(2).................................. 6
More information) 2008 8 21 22 21 10:00 12:00 e iπ 1. i ). e π. T @ MacTutor History of Mathematics archive www-history.mcs.standrews.ac.uk/history/ FAQ kawaguch))math.sci.osaka-u.ac.jp 2008 8 21 Part 1 e πi = 1 e = 2.71828...
More information日本感性工学会論文誌
Vol.13 No.2 pp.391-402 2014 PROGRESS Consideration of the Transition in Mitsubishi Electric Corporate Website Design Transition in Response to Environmental Change and Record through the Case of Corporate
More information2007/2 Vol. J90 D No Web 2. 1 [3] [2], [11] [18] [14] YELLOW [16] [8] tfidf [19] 2. 2 / 30% 90% [24] 2. 3 [4], [21] 428
Informative Summarization Method by Key Sentences Extraction Considering Sub-Topics Naoki SAGARA, Wataru SUNAYAMA, and Masahiko YACHIDA 1. 1990 WWW World Wide Web Web [15] Graduate School of Engineering
More informationa) Extraction of Similarities and Differences in Human Behavior Using Singular Value Decomposition Kenichi MISHIMA, Sayaka KANATA, Hiroaki NAKANISHI a
a) Extraction of Similarities and Differences in Human Behavior Using Singular Value Decomposition Kenichi MISHIMA, Sayaka KANATA, Hiroaki NAKANISHI a), Tetsuo SAWARAGI, and Yukio HORIGUCHI 1. Johansson
More information音声読み上げブラウザの読み上げかた
IBM 3.01, 1,234 1 HTML 2 1+1 1+1= 1 1 1 1= 1 1 1 1= 1X1 3 2004 11 14 10 2004 11 14 2004-11-14 2004/11/14 11/14 10:00 10:00am am pm a.m.p.m. 4 10 5 1 1 1 10 1 100 10 1 10 10 113 10 1 5 6 23 5372 0859 23
More information11 22 33 12 23 1 2 3, 1 2, U2 3 U 1 U b 1 (o t ) b 2 (o t ) b 3 (o t ), 3 b (o t ) MULTI-SPEAKER SPEECH DATABASE Training Speech Analysis Mel-Cepstrum, logf0 /context1/ /context2/... Context Dependent
More informationels08ws-kuroda-slides.key
NICT 26 2008/11/15, Word Sketch Engine (Kilgarriff & Tugwell 01; Srdanovic, et al. 08) ( ) I-Language Grammar is Grammar and Usage is Usage (Newmeyer 03) (is-a ) ( )?? () // () ()???? ? ( )?? ( ) Web ??
More information( ) ( ) Modified on 2009/05/24, 2008/09/17, 15, 12, 11, 10, 09 Created on 2008/07/02 1 1) ( ) ( ) (exgen Excel VBA ) 2)3) 1.1 ( ) ( ) : : (1) ( ) ( )
() ( ) Modified on 2009/05/24, 2008/09/17, 15, 12, 11, 10, 09 Created on 2008/07/02 1 1) () ( ) (exgen Excel VBA ) 2)3) 1.1 ( ) () : : (1) ( ) ( ) (2) / (1) (= ) (2) (= () =) 4)5) () ( ) () (=) (1) : (
More informationDEIM 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-like BCCWJ CD-ROM CiNii NII BCCWJ BCCWJ
-like BCCWJ CD-ROM CiNii NII BCCWJ BCCWJ BCCWJ Yahoo! Yahoo! BCCWJ BCCWJ BCCWJ BOAO PS Zipper CLASSY with Oggi Precious JJ GINZA Domani Precious Oggi ViVi GINZA BCCWJ NEXTSTEP Windows XP FD ELO KIDDIES
More information24 SPAM Performance Comparison of Machine Learning Algorithms for SPAM Discrimination
24 SPAM Performance Comparison of Machine Learning Algorithms for SPAM Discrimination 1130378 2013 3 9 SPAM SPAM SPAM SPAM SVM AdaBoost RandomForest SPAM SPAM UCI Machine Learning Repository Spambase 4601
More informationWIDE 1
WIDE 1 2 Web Web Web Web Web Web Web Web Web Web? Web Web Things to cover Web Web Web Web Caching Proxy 3 Things NOT covered / How to execute Perl Scripts as CGI binaries on Windows NT How to avoid access
More information6 ( ) 1 / 53
6 / 6 (2014 11 05 ) / 53 6 (2014 11 05 ) 1 / 53 nodeedge 2 u v u v (u, v) 6 (2014 11 05 ) 2 / 53 Twitter 6 (2014 11 05 ) 3 / 53 Facebook 6 (2014 11 05 ) 4 / 53 N N N 1,2,..., N (i, j )i j 10 i j 2(i, j
More informationInt Int 29 print Int fmt tostring 2 2 [19] ML ML [19] ML Emacs Standard ML M M ::= x c λx.m M M let x = M in M end (M) x c λx.
1, 2 1 m110057@shibaura-it.ac.jp 2 sasano@sic.shibaura-it.ac.jp Eclipse Visual Studio ML Standard ML Emacs 1 ( IDE ) IDE C C++ Java IDE IDE IDE IDE Eclipse Java IDE Java Standard ML 1 print (Int. 1 Int
More informationN-gram Language Models for Speech Recognition
N-gram Language Models for Speech Recognition Yasutaka SHINDOH ver.2011.01.22 1. 2. 3. 4. N-gram 5. N-gram0 6. N-gram 7. 2-gram vs. 3-gram vs. 4-gram 8. 9. (1) name twitter id @y_shindoh web site http://quruli.ivory.ne.jp/document/
More information_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 informationfiš„v5.dvi
(2001) 49 2 293 303 VRML 1 2 3 2001 4 12 2001 10 16 Web Java VRML (Virtual Reality Modeling Language) VRML Web VRML VRML VRML VRML Web VRML VRML, 3D 1. WWW (World Wide Web) WWW Mittag (2000) Web CGI Java
More information,.,. NP,., ,.,,.,.,,, (PCA)...,,. Tipping and Bishop (1999) PCA. (PPCA)., (Ilin and Raiko, 2010). PPCA EM., , tatsukaw
,.,. NP,.,. 1 1.1.,.,,.,.,,,. 2. 1.1.1 (PCA)...,,. Tipping and Bishop (1999) PCA. (PPCA)., (Ilin and Raiko, 2010). PPCA EM., 152-8552 2-12-1, tatsukawa.m.aa@m.titech.ac.jp, 190-8562 10-3, mirai@ism.ac.jp
More informationPart y mx + n mt + n m 1 mt n + n t m 2 t + mn 0 t m 0 n 18 y n n a 7 3 ; x α α 1 7α +t t 3 4α + 3t t x α x α y mx + n
Part2 47 Example 161 93 1 T a a 2 M 1 a 1 T a 2 a Point 1 T L L L T T L L T L L L T T L L T detm a 1 aa 2 a 1 2 + 1 > 0 11 T T x x M λ 12 y y x y λ 2 a + 1λ + a 2 2a + 2 0 13 D D a + 1 2 4a 2 2a + 2 a
More informationII II,,,, AII BII CII
2012 Course Design of 2nd Semester (2012 9 24 ) 2012 1 II........................... 3 II,,,,.... 4 2 AII........................... 5 BII.......................... 6 CII.......................... 7 CIII...........................
More information<4D6963726F736F667420576F7264202D204850835483938376838B8379815B83578B6594BB2D834A836F815B82D082C88C60202E646F63>
誤 り 訂 正 技 術 の 基 礎 サンプルページ この 本 の 定 価 判 型 などは, 以 下 の URL からご 覧 いただけます http://wwwmorikitacojp/books/mid/081731 このサンプルページの 内 容 は, 第 1 版 発 行 時 のものです http://wwwmorikitacojp/support/ e mail editor@morikitacojp
More information一般社団法人電子情報通信学会 THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGIN
一般社団法人電子情報通信学会 THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS 信学技報 IEICE Technical Report SP2019-12(2019-08)
More informationBloomfield, L. (1933). Language. Chomsky, N. (1957). Syntactic structures. George Allen and Unwin. Mouton. Chomsky, N. (1964). Current issues in linguistic theory. Mouton. Chomsky, N. (1965). Aspects of
More informationWPA(Win Probability Added) 1 WPA WPA ( ) WPA WPA WPA WPA WPA
21 4 25 1 31 WPA(Win Probability Added) 1 WPA WPA ( ) WPA WPA WPA WPA WPA 1 1 2 WPA 3 2.1 WPA(Win Probability Added)................................. 3 2.2........................... 3 2.2.1...................................
More information. p.1/34
. p.1/34 (Optimization) (Mathematical Programming),,. p.2/34 1 1.1 1.2 1.3 2 2.1 2.2 2.3 2.4 2.5 3 4 5. p.3/34 1 1.1 1.2 1.3 2 2.1 2.2 2.3 2.4 2.5 3 4 5. p.4/34 4x + 2y 6, 2x + y 6, x 0, y 0 x, yx + yx,
More information直交座標系の回転
b T.Koama x l x, Lx i ij j j xi i i i, x L T L L, L ± x L T xax axx, ( a a ) i, j ij i j ij ji λ λ + λ + + λ i i i x L T T T x ( L) L T xax T ( T L T ) A( L) T ( LAL T ) T ( L AL) λ ii L AL Λ λi i axx
More informationIPSJ SIG Technical Report Vol.2009-CVIM-167 No /6/10 Real AdaBoost HOG 1 1 1, 2 1 Real AdaBoost HOG HOG Real AdaBoost HOG A Method for Reducing
Real AdaBoost HOG 1 1 1, 2 1 Real AdaBoost HOG HOG Real AdaBoost HOG A Method for Reducing number of HOG Features based on Real AdaBoost Chika Matsushima, 1 Yuji Yamauchi, 1 Takayoshi Yamashita 1, 2 and
More information/* sansu1.c */ #include <stdio.h> main() { int a, b, c; /* a, b, c */ a = 200; b = 1300; /* a 200 */ /* b 200 */ c = a + b; /* a b c */ }
C 2: A Pedestrian Approach to the C Programming Language 2 2-1 2.1........................... 2-1 2.1.1.............................. 2-1 2.1.2......... 2-4 2.1.3..................................... 2-6
More informationjohnny-paper2nd.dvi
13 The Rational Trading by Using Economic Fundamentals AOSHIMA Kentaro 14 2 26 ( ) : : : The Rational Trading by Using Economic Fundamentals AOSHIMA Kentaro abstract: Recently Artificial Markets on which
More information2001 Miller-Rabin Rabin-Solovay-Strassen self-contained RSA RSA RSA ( ) Shor RSA RSA 1 Solovay-Strassen Miller-Rabin [3, pp
200 Miller-Rabin 2002 3 Rabin-Solovay-Strassen self-contained RSA RSA RSA ( ) Shor 996 2 RSA RSA Solovay-Strassen Miller-Rabin [3, pp. 8 84] Rabin-Solovay-Strassen 2 Miller-Rabin 3 4 Miller-Rabin 5 Miller-Rabin
More information¥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橡ボーダーライン.PDF
1 ( ) ( ) 2 3 4 ( ) 5 6 7 8 9 10 11 12 13 14 ( ) 15 16 17 18 19 20 ( ) 21 22 23 24 ( ) 25 26 27 28 29 30 ( ) 31 To be or not to be 32 33 34 35 36 37 38 ( ) 39 40 41 42 43 44 45 46 47 48 ( ) 49 50 51 52
More informationx, y x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = 15 xy (x y) (x + y) xy (x y) (x y) ( x 2 + xy + y 2) = 15 (x y)
x, y x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = 15 1 1977 x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = 15 xy (x y) (x + y) xy (x y) (x y) ( x 2 + xy + y 2) = 15 (x y) ( x 2 y + xy 2 x 2 2xy y 2) = 15 (x y) (x + y) (xy
More information1 IDC Wo rldwide Business Analytics Technology and Services 2013-2017 Forecast 2 24 http://www.soumu.go.jp/johotsusintokei/whitepaper/ja/h24/pdf/n2010000.pdf 3 Manyika, J., Chui, M., Brown, B., Bughin,
More informationI, II 1, A = A 4 : 6 = max{ A, } A A 10 10%
1 2006.4.17. A 3-312 tel: 092-726-4774, e-mail: hara@math.kyushu-u.ac.jp, http://www.math.kyushu-u.ac.jp/ hara/lectures/lectures-j.html Office hours: B A I ɛ-δ ɛ-δ 1. 2. A 1. 1. 2. 3. 4. 5. 2. ɛ-δ 1. ɛ-n
More informationE 2017 [ 03] (DAG; Directed Acyclic Graph) [ 13, Mori 14] DAG ( ) Mori [Mori 12] [McDonald 05] [Hamada 00] 2. Mori [Mori 12] Mori Mori Momouchi
Original Paper Extracting Semantic Structure from Procedual Texts Hirokuni Maeta Yoko Yamakata Shinsuke Mori Cybozu, Inc. hirokuni.maeta@gmail.com Graduate School of Information Science and Technology,
More information3807 (3)(2) ,267 1 Fig. 1 Advertisement to the author of a blog. 3 (1) (2) (3) (2) (1) TV 2-0 Adsense (2) Web ) 6) 3
Vol. 52 No. 12 3806 3816 (Dec. 2011) 1 1 Discovering Latent Solutions from Expressions of Dissatisfaction in Blogs Toshiyuki Sakai 1 and Ko Fujimura 1 This paper aims to find the techniques or goods that
More information1 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 informationIsogai, T., Building a dynamic correlation network for fat-tailed financial asset returns, Applied Network Science (7):-24, 206,
H28. (TMU) 206 8 29 / 34 2 3 4 5 6 Isogai, T., Building a dynamic correlation network for fat-tailed financial asset returns, Applied Network Science (7):-24, 206, http://link.springer.com/article/0.007/s409-06-0008-x
More information数学Ⅱ演習(足助・09夏)
II I 9/4/4 9/4/2 z C z z z z, z 2 z, w C zw z w 3 z, w C z + w z + w 4 t R t C t t t t t z z z 2 z C re z z + z z z, im z 2 2 3 z C e z + z + 2 z2 + 3! z3 + z!, I 4 x R e x cos x + sin x 2 z, w C e z+w
More information[12] Qui [6][7] Google N-gram[11] Web ( 4travel 5, 6 ) ( 7 ) ( All About 8 ) (1) (2) (3) 3 3 (1) (2) (3) (a) ( (b) (c) (d) (e) (1
RD-003 Building a Database of Purpose for Action from Word-of-mouth on the Web y Hiromi Wakaki y Hiroko Fujii y Michiaki Ariga y Kazuo Sumita y Kouta Nakata y Masaru Suzuki 1 ().com 1 Amazon 2 3 [10] 2007
More information11_寄稿論文_李_再校.mcd
148 2011.4 1 4 Alderson 1996, Chapelle 2001, Huston 2002, Barker 2004, Rimmer 2006, Chodorow et al. 2010 He & Dai 2006 2 3 4 2 5 4 1. 2. 3. 1 2 (1) 3 90 (2) 80 1964 Brown 80 90 British National Corpus
More information004139 医用画像‐27‐3/★追悼文‐27‐3‐0 松本様
12 13 1 vii 2 x 3 xii 4 xiv 5 xvii 6 xx 7 xxii 8 xxvii 9 xxix 10 xxxi 11 xxxii vi X 1950 69 X 1964 RII RII 2 [1, 2] [3] [4] X 1953 P.Elias OTF [5] OTF X 1962 ICO OTF RII X I-m M-n m n X X RII 1 1964 3
More informationDuality in Bayesian prediction and its implication
$\theta$ 1860 2013 104-119 104 Duality in Bayesian prediction and its implication Toshio Ohnishi and Takemi Yanagimotob) a) Faculty of Economics, Kyushu University b) Department of Industrial and Systems
More informationIPSJ SIG Technical Report On a Bayesian Network-based Model for Referring Expressions Kotaro Funakoshi, 1 Mikio Nakano, 1 Takenobu Tokunaga 2
1 1 2 2 On a Bayesian Network-based Model for Referring Expressions Kotaro Funakoshi, 1 Mikio Nakano, 1 Takenobu Tokunaga 2 and Ryu Iida 2 A Bayesian network-based model available both for resolution and
More information