I HC QUC GIA H NI

TRNG I HC CNG NGH

NGUYN TH THA

PHN LOI CU TING VIT

V NG DNG TRONG VN HI P

LUN VN THC S CNG NGH THNG TIN

H Ni - 2015

I HC QUC GIA H NI

TRNG I HC CNG NGH

NGUYN TH THA

PHN LOI CU TING VIT

V NG DNG TRONG VN HI P

Ngnh : Cng ngh thng tin

Chuyn ngnh : H thng thng tin

M s : 60 48 01 04

LUN VN THC S CNG NGH THNG TIN

GIO VIN HNG DN KHOA HC: TS. PHAN XUN HIU

Hc vin thc hin Gio vin hng dn Hi ng chm lun vn

H Ni 2015

LI CAM OAN

Ti Nguyn Th Tha xin cam oan ni dung trong lun vn ny l cng

trnh nghin cu v sng to do chnh ti thc hin di s hng dn ca TS.

Phan Xun Hiu. S liu, kt qu trnh by trong lun vn l hon ton trung thc

v cha cng b trong bt c cng trnh khoa hc no trc y. Nu hnh nh

c ly t ngun bn ngoi, ti u c trch dn ngun r rng v y .

H Ni, ngy thng nm 2015

Hc vin

Nguyn Th Tha

2

LI CM N

u tin, ti xin gi li cm n chn thnh n thy Phan Xun Hiu. Thy

truyn cm hng hc tp, nhit huyt nghin cu khoa hc v dn li ti

n vi lnh vc nghin cu ny. Thy cng l ngi tn tnh gip ti vt

qua nhng th thch trong qu trnh nghin cu lun vn.

Ti xin gi li cm n chn thnh n thy H Quang Thy. Cng tip xc

vi thy, ti cng cm thy yu qu v trn trng thi gian c lm sinh vin

nhiu hn.

Ti xin by t lng bit n chn thnh ti cc thy, c gio ging dy ti

trong sut 2 nm ti Trng i hc Cng ngh - i hc Quc gia H Ni. Mi

thy c u cho ti nhng bi ging tht hay v b ch.

Ti xin cm n cc anh ch trong Phng o to, Phng Cng tc sinh vin,

Phng Ti v v cc anh ch khc trong trng. Nh c s lm vic tn ty ca

cc anh ch, chng ti mi c mt ngi trng ng nht nh c nc hc tp

v rn luyn.

Ti xin by t s cm n su sc n cc thnh vin trong nhm MDN-

Team. Thi gian chng ti bn nhau chia s nhng kh khn khi to ra ng

dng tr l o cho ngi Vit - VAV. c bit l 2 em Nguyn Vn Hp v V

Th Hi Yn nhit tnh gip ti trong qu trnh thc nghim, ti s khng

bao gi qun.

Ti xin gi li cm n chn thnh cc anh ch ng nghip ti Cc Thng

tin khoa hc v cng ngh quc gia B Khoa hc v Cng ngh gip hon

thnh cng vic ti c quan ti c th yn tm hc tp.

Ti cng xin cm n cc anh ch trong Phng Th nghim cng ngh tri

thc gp chi tit mi bui seminar hng tun ti hon thin tt lun vn

ca mnh.

Cui cng, ti xin chn thnh cm n n b m, anh ch trong gia nh.

H l ngun ng vin khng th thiu trong cuc i ti.

H Ni, ngy thng nm 2015

Hc vin

Nguyn Th Tha

3

MC LC

T VN ................................................................................................................. 6

Chng I. Gii thiu v phn loi cu v ng dng ................................................ 14

1.1 Cc cng trnh nghin cu v phn loi cu .................................................... 14

1.2. Phn loi cu ting Vit ................................................................................... 16

1.2.1. Gii thiu v bi ton Phn loi cu ting Vit .................................... 16

1.2.2. Cc phng php gii quyt bi ton ................................................. 18

Chng II. Phn loi cu ting Vit bng cc phng php hc my ................... 19

2.2. Phng php Nave Bayes ............................................................................... 19

2.3. Phng php SVMs ......................................................................................... 21

2.4. Thut ton Maximum Entropy ......................................................................... 23

Chng III. Thc nghim ........................................................................................... 26

3.1. Phng php thc nghim ............................................................................... 26

3.2. D liu thc nghim ........................................................................................ 28

3.3. La chn thuc tnh ......................................................................................... 29

3.4. Kt qu thc nghim v phn tch ................................................................... 30

3.4.1. M hnh MaxEnt .............................................................................. 30

3.4.2. M hnh Nave Bayes ....................................................................... 33

3.4.4. So snh MaxEnt, Nave Bayes v SVMs ............................................ 36

KT LUN ............................................................................................................................. 38

TI LIU THAM KHO ...................................................................................................... 39

PH LC ................................................................................................................................ 41

4

DANH SCH HNH V

Hnh 0.1 Giao din phn mm ng dng VAV Tr l o cho ngi Vit

Hnh 0.2 Ngun d liu cho Big Data

Hnh 0.3 Giao din phn mm VOS

Hnh 1.1 M hnh n gin bi ton phn loi cu ting Vit

Hnh 1.2 V d minh ho bi ton phn loi cu ting Vit

Hnh 1.3 M hnh tng th bi ton phn loi cu ting Vit

Hnh 2.1 M hnh SVMs

Hnh 3.1 Phng php Cross Validation Test

Hnh 3.2 S lng mi loi cu thu c qua ASR service (Google Voice)

Hnh 3.3 Biu so snh o F1 ca m hnh MaxEnt trn 2 tp thuc tnh

ln lp th 4

Hnh 3.4 Biu so snh F1 ca m hnh Nave Bayes gia 2 tp thuc tnh n-

grams v n-grams + Dictionary

Hnh 3.5 Biu so snh o F1 ca m hnh SVMs gia 2 tp thuc tnh n-

grams v n-grams + Dictionary sau 4 folds

Hnh 3.6 Biu so snh o F1 ca 3 m hnh MaxEnt, Nave Bayes v

SVMs ln lp th 4 trn tp thuc tnh n-grams

Hnh 3.7 Biu so snh o F1 ca 3 m hnh MaxEnt, Nave Bayes v

SVMs ln lp th 4 trn tp thuc tnh n-grams + Dictionary

Hnh PL.1 S phn b d liu khi Phn loi vi phng php Nave Bayes

Hnh PL.2 Kt qu Phn loi vi phng php Nave Bayes

Hnh PL.3 S phn b d liu khi Phn loi vi phng php SVMs

Hnh PL.4 Kt qu Phn loi vi phng php SVMs

Hnh PL.5 D liu u vo fold th 4 vi phng php MaxEnt

Hnh PL.6 D liu hun luyn fold 4

Hnh PL.7 D liu kim tra fold 4

Hnh PL.8 Kt qu nh gi m hnh MaxEnt

Hnh PL.9 S phn b d liu khi Phn loi vi phng php Nave Bayes

Hnh PL.10 Kt qu Phn loi vi phng php Nave Bayes

Hnh PL.11 S phn b d liu khi Phn loi vi phng php SVMs

Hnh PL.12 Kt qu Phn loi vi phng php SVMs

Hnh PL.13 D liu hun luyn fold 4

Hnh PL.14 D liu kim tra fold 4

Hnh PL.15 Kt qu nh gi m hnh MaxEnt

5

DANH SCH BNG BIU

Bng 1.1 Bng m t cc kiu cu thng dng

Bng 3.1 Mt s thuc tnh mu hun luyn m hnh phn loi cu

Bng 3.2 Kt qu ln lp th 4 ca m hnh MaxEnt vi tp thuc tnh n-grams

Bng 3.3 Kt qu ln lp th 4 ca m hnh MaxEnt vi tp thuc tnh n-grams

+ Dictionary

Bng 3.4 Kt qu tng ln lp ca m hnh MaxEnt vi tp thuc tnh n-grams

Bng 3.5 Kt qu tng ln lp ca m hnh MaxEnt vi tp thuc tnh n-grams

+ Dictionary

Bng 3.6 Kt qu sau 4 ln lp ca m hnh Nave Bayes vi tp thuc tnh n-

grams

Bng 3.7 Kt qu sau 4 ln lp ca m hnh Nave Bayes vi tp thuc tnh n-

grams + Dictionary

Bng 3.8 Kt qu sau 4 ln lp ca m hnh SVMs vi tp thuc tnh n-grams

vi C = 0.1, gamma = 0.5, Kernel = exp (-gamma*|u-v|^2)

Bng 3.9 Kt qu sau 4 ln lp ca m hnh SVMs vi tp thuc tnh n-grams +

Dictionary vi C = 0.1, gamma = 0.5, Kernel = exp (-gamma*|u-v|^2)

6

T VN

Theo PGS.TS. Bi Mnh Hng [1], thc hin mc ch pht ngn, ngi

ta thng dng cu trc c php c trng kt hp vi nhng phng tin ngn

ng ring bit nh: tiu t, ph t, ph t, trt t t, ng iu, hin tng tnh

lc, v.v. Ngha l c mt mi tng quan kh u n gia hnh thc ca cu v

mc ch s dng n. T hnh thnh nn khi nim kiu cu (sentence type)

v nhng kiu cu thng dng nht thng c nhc n l: cu trn thut, cu

nghi vn, cu cu khin, cu cm thn (x. J. Sadock & A. Zwicky 1990: 155-156).

Phn loi cu ting Vit bng my tnh l bi ton c bn, lm tin cho

cc nghin cu cao hn v x l v hiu ngn ng t nhin. Phn loi cu l mt

trong nhng thnh phn x l ct li ca h thng hi p nh phn mm ng

dng VAV (Vitual Assistant for Vietnammese) Tr l o cho ngi Vit do

MDN Team thuc Trng i hc Cng ngh - i hc Quc gia H Ni sng

lp, ca h thng phn tch social media nghin cu th trng nh cc h

thng x l Big Data hay trong h thng tng hp ting ni nh VOS Ting ni

Phng Nam do i hc Quc gia Tp. H Ch Minh sng lp.

Hnh 0.1 Giao din phn mm ng dng VAV Tr l o cho ngi Vit

7

VAV l mt ng dng thng minh trn di ng cho php ngi dng tng

tc bng ging ni hn chung bo thc, t lch cho mt cuc hp, bt nh

v, gi in cho ai , truy cp mt trang web bt k, tm ng trn bn , nh

v cy ATM ca mt ngn hng no gn vi bn, hay thng thc mt bn

nhc mnh yu thch c thit k v pht trin da trn cc k thut tr tu

nhn to (hc my, phn tch v hiu ngn ng t nhin), VAV c th hiu c

nh ca ngi dng d h din t cu lnh ca mnh theo nhiu cch khc

nhau m khng cn tun theo bt k khun mu no cho trc.

VAV - ng dng tr l o cho ngi Vit l mt trong nhng phn mm

nhn c nhiu s quan tm trn cc trang mng x hi, cc din n cng ngh.

Phn loi cu gip VAV lc ra c nhng cu thuc kiu cu hi hoc kiu cu

cu khin x l tip tc cc pha tip theo hoc VAV s hi p li ngay cho

ngi dng m khng cn x l nu l cu cm thn hoc cu trn thut qua

module h tr tch hp sn trong VAV.

Big data l tp hp d liu ln v a dng nn khng th x l bng cch

th cng hoc bng phn mm thng thng. Vic thu thp, qun l, phn tch d

liu ny tr thnh ngnh ring trong cng ngh thng tin v thu ht c s

ch ca gii kinh doanh trong nhng nm gn y v tim nng ca n.

Hnh 0.2 Ngun d liu cho Big Data

8

Social media ch trong thi gian ngn to nn lng d liu bng lng

d liu ca c th gii vi th h trc: Facebook mi ngy u x l 500 terabytes

d liu, Twitter mi ngy cng x l 12 terabytes d liu; trong khi sn chng

khon New Yorks ch x l 1 terabytes d liu. Lng d liu t Social Media s

l m vng i vi cc doanh nghip mun hiu v hnh vi khch hng ca mnh,

cch h a ra quyt nh mua sm, nhu cu ca h trong tng lai gn...

Phn loi cu trong trng hp ny s gip h thng lc ra c nhng cu

no th hin trng thi tm l ca ngi dng, nhng cu no phn nh s khen

ch t doanh nghip s c th a ra gii php ci tin sn phm ca h

hoc c nhng chin lc thu ht khch hng kp thi.

Tng t, trong h thng tng hp ting ni, Ting ni Phng Nam VOS

l mt h thng tng hp ting ni ting Vit, dnh cho chnh ngi Vit, c th

to ra ging ni nhn to ca ngi trn my tnh t d liu u vo l vn bn.

Phn loi cu lc ny s gip h thng thm c sc thi cho cu vn trong on

text .

Trong lnh vc truyn thng, h thng VOS c th c p dng trong cc

ng dng truy vn thng tin qua tng i in thoi, trong yu cu ca ngi

dng s c ng dng tip nhn v x l thnh dng vn bn. Thng tin ny s

c h thng VOS chuyn thnh dng m thanh v tr v cho ngi dng. Cc

h thng ny c kh nng ng dng cao do qu trnh x l hon ton t ng, c

th hot ng lin tc, p ng c nhu cu v thng tin ca ngi dng, c

bit l cc thng tin nng, cp nht.

Trong lnh vc t ng ha, h thng VOS c th c tch hp vi h thng

nh v GPS trong cc ng dng tm ng i, gn trn xe hi cung cp cc ch

dn dng m thanh, hn ch vic li xe phi lin tc va nhn mn hnh GPS,

lm tng an ton cho ngi iu khin.

Trong lnh vc gio dc, VOS c th c s dng dy ting Vit cho

con em Vit Kiu nh c nc ngoi, nht l cch c, cch pht m cc t

ting Vit. y l phn mm thc hnh ting Vit hu hiu, c bit trong mi

trng m ngn ng s dng khng phi l ting Vit.

39

TI LIU THAM KHO

Ti liu ting Vit

[1] Bi Mnh Hng (2011), Bn v vn Phn loi cu theo mc ch pht

ngn, Khoa Ngn ng, i hc Quc gia Tp. H Ch Minh.

[2] Bi c Tnh (1995), Vn phm Vit Nam. Tp. H Ch Minh: Vn ha.

[3] Hong Trng Phin (1980), Ng php ting Vit Cu. H Ni: i hc &

Trung hc chuyn nghip.

[4] Nguyn H Nam (2013), Gio trnh Khai ph d liu, Nh Xut bn

i hc Quc gia H Ni.

Ti liu ting Anh

[5] Adam L. Berger & Stephen A.Della Pietra & Vincent J. Della Pietra (1996),

A Maximum Entropy Approach to Natural Language Processing.

[6] Adwait Ratnapakhi (1997), A Simple Introduction to Maximum Entropy

Models for Natural Language Processing.

[7] Ashequl Qadir (2011), Classifying Sentences as Speech Acts in Message

Board Posts, University of Utah, In Proceedings of the 2011 Conference

on Empirical Methods in Natural Language Processing.

[8] Arpit Trived (2013), Implementation of Bayesian Theory in Sentence

Classification for Online Subjective Test, International Journal of

Advanced Research in Computer Science and Software Engineering,

Volume 3, Issue 12.

[9] Anthony Khoo (2006), Experiments with Sentence Classification, Monash

University, Australia.

[10] Ben Hachey & Claire Grover (2004), Sentence Classification Experiments

for Legal Text Summarisation, University of Edinburgh, In Proceedings of

the 17th Annual Conference on Legal Knowledge and Information

Systems.

[11] Diego Molla (2012), Experiments with Clustering-based Features for

Sentence Classification in Medical Publications: Macquarie Tests

participation in the ALTA 2012 shared task, In Proceedings of Australasian,

Language Technology Association Workshop, pages 139142.

40

[12] Helen Kwong (2012), Detection of Imperative and Declarative Question-

Answer Pairs in Email Conversations, Stanford University, Journal AI

Communications archive, Volume 25 Issue 4, Pages 271-283.

[13] Martina Naughton (2008), Sentence-Level Event Classification in

Unstructured Texts, University College Dublin, Ireland.

[14] Menno v.an Zaanen (2005), Classifying Sentences using Induced Structure,

Macquarie University, Volume 3772 of the series Lecture Notes in

Computer Science, pp 139-150, 12th International Conference, SPIRE

2005, Buenos Aires, Argentina.

[15] Nal Kalchbrenner (2014),A Convolutional Neural Network for Modelling

Sentences, University of Oxford, In Proceedings of the 52nd Annual

Meeting of the Association for Computational Linguistics.

[16] William Gardner Hale (1913), The Classification of Sentences and Clauses,

The School Review, The University of Chicago Press, Vol. 21, No. 6, pp.

388-397.

[17] Ulf Hermjakob (2001),Parsing and Question Classification for Question

Answering, University of Southern California, USA, Proceeding ODQA '01

Proceedings of the workshop on Open-domain question answering -

Volume 12, Pages 1-6.

[18] Yoon Kim (2014), Convolutional Neural Networks for Sentence

Classification, New York University.

[19] Emile de Maat (2008), Automatic Classification of Sentences in Dutch

Laws, University of Amsterdam, Proceedings of the 2008 conference on

Legal Knowledge and Information Systems, The Twenty-First Annual

Conference,Pages 207-216

[20] Janyce Wiebe (2005), Creating Subjective and Objective Sentence

Classifiers from Unannotated Texts, University of Pittsburgh, CICLing'05

Proceedings of the 6th international conference on Computational

Linguistics and Intelligent Text Processing, Pages 486-497.

[21] Nitin Jindal (2006), Identifying Comparative Sentences in Text Documents,

University of Illinois at Chicago, SIGIR06.

[22] Thomasson, Amie, "Categories", The Stanford Encyclopedia of Philosophy

(Fall 2013 Edition), First published Thu Jun 3, 2004, URL =

.