문서 분류의 이론과 실제

문서 분류의 이론과 실제

Text Categorization ([email protected]) (Document Representation) (Similarity Measure) (Document Clustering)) (Text Categ)orization)

(Information Filtering)) (Topic Detection) (Topic Tracking)) 2 vs vs vs vs 3

Document Representation (multi-dimensional space) (document) (vector) (dimension) (term) (concept) system Doc2 Doc1 query retrieval Doc6 information

Doc5 Doc3 Doc4 5 (rendering)) . More than 150 former officers of the overthrown South Vietnamese g)overnment have been released from a re-education camp after 13 years of detention, the official Vietnam News Ag)ency reported Saturday. / 6

/ncn + /jca -> (stop word) a, the, this, (stemming)) swimming), swims, swimmer > swim flowers > flower [IR92,pp.151~160] William B. Frakes, Information Retrieval Data Structures & Algorithms, Prentice-Hall, 1992. 7

< , > (rendering)) . /mmd /ncn+ /jca+ /jxc /ncn+ /jco /pvg)+ /etm /paa+ /etm /ncn (/sl rendering)/f )/sr /ncn+ /jco /ncpa+ /xsv+ /ef ./sf render

0.007 0.047 0.041 0.034 0.007 0.008 8 ? ? ? 2 2 ?

9 (weighting scheme) (tf: term frequency) tf t t (idf: inverse document frequency) N idf t log 2 df t tfidf N : dft : t

weightt tf t idf t 10 SMART (term frequency) (doc. frequency) nnn n : tf tf a : 0.5 0.5 max tf l : ln tf 1.0

(normalization) c: ntn ntc ann n : 1.0 N t : ln n n : 1.0 nnc anc atn atc

lnn 1 2 w vector i lnc ltn ltc 11 - d1 N : N : < > 0

: < , > rende r

d1 0.007 0.047 0.041 render 0.034 0.007 0.008 d2 0.00 0 7 0.002 0.003 0 0 0.015

0.04 0.041 7 0 0.04 0 0.007 1 0.03 0 4 0 0 0.00 7 d2 0 0.00 0.002 0.003 8

0.015 0 0.041 0 0.007 12 - , < , > , df +1 , tfidf < , >

, d1 render0.034 0.007 0.041 0.007 0.047 0.008 d2 < 0.041 0.002 0.015 0.007 0.003 d1 render

d2 13 (rank) [Lee, SIGIR 95] [Lee, SIGIR 96] 14 Similarity Measure

(similarity measure) (Cosine coefficient) (Jaccard coefficient) (Dice coefficient) (Euclidean distance) (vector inner product) 16 (Cosine coefficient) 0~1 1: 0: n w ik

sim( d i , d j ) w jk k 1 n n 2 w w jk k 1 di wi1 , wi 2 , , win dj

w j1 , w j 2 , , w jn 2 ik k 1 17 (Jaccard coefficient) n w ik sim( d i , d j ) w jk

k 1 n 2 2 w w ik jk k 1 n w ik w jk k 1

(Dice coefficient) n sim( d i , d j ) 2 wik w jk k 1 n 2 2 w w ik jk k 1 18

(inner product) - n sim( d i , d j ) wik w jk k 1 (Euclidean distance) Ward n dist ( d i , d j ) 2 ( w w

) ik jk k 1 19 sim( x, y ) sim( x, y ) sim( x, y ) f xy f x f y f xy

f x f y f xy 2 f xy fx fy 20 Ward Voorhees 21

[ , 1999] , , , , 22 Text Categorization (Lewis 1992) ?

24 15219 <contents> . . , , . : <!--page-separator--></p> <p> . , , , , . : . 1 . , , , , , . 25 <!--page-separator--></p> <p> (feature extractor) (text classifier) <!--page-separator--></p> <p> 1 2 n 26 [Lewis 1992] ( ) <!--page-separator--></p> <p> ( ) 27 [Fag)an 1987] , <!--page-separator--></p> <p> 28 [Lewis 1992] black market, 29 <!--page-separator--></p> <p> [Li1997,Guthrie1994,Baker 1998] kick, g)oal, ball Hard clustering) vs. Soft clustering) [Lewis 1992, 1997] , 30 [ 1999] : <!--page-separator--></p> <p> 150 87 63 3,990 - : wordnet: http://wordnet.princeton.edu/ 31 <!--page-separator--></p> <p> , 32 2 [Devore 1995] 2 33 <!--page-separator--></p> <p> 2 c a <!--page-separator--></p> <p> b d a b a+c = (R) a+b = (df) a+b+c+d = (N) 34 2 <!--page-separator--></p> <p>[Devore 1995] (a+b+c+d) (ad-bc)2 2 = (a+c)(b+d)(a+b)(c+d) c b a d <!--page-separator--></p> <p> 2 , 35 (k-Nearest Neig)hbors) - (Linear Text Classifiers) Rocchio (Decision Tree) (Support Vector Machines) (Bayesian classifier) (Neural Network classifier) 36 K- (k-Nearest Neig)hbors) <!--page-separator--></p> <p>Memory-Based Reasoning) <d1, C1>, <d2, Ck>,<d3, C1,C2>, d d k (k- ) 37 K-NN [1] [2] k [3] k 1 <!--page-separator--></p> <p> d n 38 k k 39 <!--page-separator--></p> <p> - Liner Text Classifiers - 1 n 40 - [Lewis 1996] Rocchio algorithm Widrow-Hoff alg)orithm <!--page-separator--></p> </div> <div class="col-md-4" style="overflow: hidden;"> <p> EG(Exponentiated-g)radient) alg)orithm 41 Rocchio Rocchio 1960 - - D D <!--page-separator--></p> <p>DRe l DRe l Qopt R N R R: N: 42 Rocchio , Rocchio <!--page-separator--></p> <p>D D Qnew Qorg DRe l DRe l R N R , , 43 [Decision Tree] C4.5 [Quinlan 1993] (pruning)) <!--page-separator--></p> <p> / 44 : Play, Don't Play. outlook: sunny, overcast, rain. temperature: continuous. humidity: continuous. windy: true, false. Decision Tree: outlook = overcast: Play outlook = sunny: | humidity <= 75 : Play <!--page-separator--></p> <p>| humidity > 75 : Don't Play outlook = rain: | windy = true: Don't Play | windy = false: Play : sunny, 85, 85, false, Don't Play sunny, 80, 90, true, Don't Play overcast, 83, 78, false, Play rain, 70, 96, false, Play rain, 68, 80, false, Play rain, 65, 70, true, Don't Play overcast, 64, 65, true, Play sunny, 72, 95, false, Don't Play sunny, 69, 70, false, Play rain, 75, 80, false, Play sunny, 75, 70, true, Play overcast, 72, 90, true, Play overcast, 81, 75, false, Play rain, 71, 80, true, Don't Play 45 <!--page-separator--></p> <p> - (Support Vector Machines) (SV) , SVMlight http://svmlig)ht.joachims.org)/ C , : University of Dortmund, Informatik, AI-Unit Collaborative Research Center on 'Complexity Reduction in Multivariate Data' (SFB475) svm_learn svm_classify / +1/-1 46 - <!--page-separator--></p> <p>Rainbow Naive Bayes k-nearest neig)hbor Rocchio Maximum Entropy Andrew McCallum [CMU] http://www-2.cs.cmu.edu/~mccallum/bow/ rainbow/ 47 23,113 , 12 , 76 12 0100. 0200. 0300. 0400. <!--page-separator--></p> <p>0500. 0600. 0700. 0800. 0900. 1000. 1100. 1200. . <!--page-separator--></p> <p>0401. 0402. 0403. 0404. 0405. ... 76 0601. 0602. 0603. 06 04. 0605. 0606. 0607. <!--page-separator--></p> <p>0608. 0609. ... 48 Topic Detection TDT Evaluation Project TDT(Topic Detection & Tracking)) , , , DARPA , TIDES(Translingual Information Detection, Extraction, and Summarization) TDT1 3 ( CMU, Umass, Drag)on systems), TDT4 1999 , 2004 <!--page-separator--></p> <p> http://www.nist.g)ov/speech/tests/tdt/tides.htm 50 TDT Evaluation Project 5 Story Segmentation - Detect changes between topically cohesive sections Topic Tracking - Keep track of stories similar to a set of example stories Topic Detection - Build clusters of stories that discuss the same topic <!--page-separator--></p> <p> First Story Detection - Detect if a story is the first story of a new, unknown topic Link Detection - Detect whether or not two stories are topically linked 51 (topic) (event) (event) , USAir-427 : : 52 <!--page-separator--></p> <p> vs (query) , ? ? 53 vs <!--page-separator--></p> <p> 54 vs / 55 vs , <!--page-separator--></p> <p> 56 d1 d2 dN d1 T1 T1 <!--page-separator--></p> <p>d2 T1 T1 T2 T1 57 (threshold) ? T1 <!--page-separator--></p> <p> T2 T3 di ? Tm T1 T2 T3 Tm <!--page-separator--></p> <p> di 58 : d1, d2, , dN (d1 T1) 2~N (d2, , dN) (T1, T2, Tk) , Tk+1 , , <!--page-separator--></p> <p> / / 59 vs : : , vs : 61 vs ( ) : <!--page-separator--></p> <p> vs ( ) 62 </p> </div> </div> </div> </section> <section class="main-section"> <div class="container"> <script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js"></script><!-- Responsive --><ins class="adsbygoogle" style="display:block" data-ad-client="ca-pub-3757518916489959" data-ad-slot="1838143700" data-ad-format="auto" data-full-width-responsive="true"></ins><script>(adsbygoogle = window.adsbygoogle || []).push({});</script> </div> </section> <section class="main-section"> <div class="container"> <div class="document-others"> <h2 class="section-title text-center">Related Presentations</h2> <div class="row"> <div class="col-lg-3 col-md-4 col-12 grid-margin stretch-card mb-4"> <div class="card"> <div class="card-body"> <a href="http://vietnoodlebar.net/slide/freshman-seminar-daily-agenda-p90pwt" title="Freshman Seminar Daily Agenda"> <img src="/assets/img/default.png" data-src="https://cdn.slideread.com/cover/freshman-seminar-daily-agenda-p90pwt.jpg" alt="Freshman Seminar Daily Agenda" title="Freshman Seminar Daily Agenda" class="lazy img-fluid"> </a> </div> <div class="card-footer text-center"> <a class="mt-auto my-1" href="http://vietnoodlebar.net/slide/freshman-seminar-daily-agenda-p90pwt" title="Freshman Seminar Daily Agenda">Freshman Seminar Daily Agenda</a> </div> </div> </div> <div class="col-lg-3 col-md-4 col-12 grid-margin stretch-card mb-4"> <div class="card"> <div class="card-body"> <a href="http://vietnoodlebar.net/slide/rubrik-ltuse-bwbqri" title="Rubrik - 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