ो. 34 ो. 34 ो. 34 -...

Page | 0

---- (((( $$$$ &&&& ))))

LEXICAL AMBIGUTIY IN HINDI-MARATHI MACHINE TRANSLATION (IN THE

CONTEXT OF HOMONYMS)

((((....++++.... ---- //// ---- ////))))

4444

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JJJJ 110067110067110067110067

2009200920092009

Page | 1

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. . .. . .. . .. . .

Page | 2

Tkokgjyky usg: foTkokgjyky usg: foTkokgjyky usg: foTkokgjyky usg: fo''''ofo|ky;ofo|ky;ofo|ky;ofo|ky;

JAWAHARLAL NEHRU UNIVERSITY

Centre of Indian Languages,

School of Language, Literature & Culture Studies,

New Delhi 110067, India

_______________________________________________________________________________

Centre of Indian Languages DATE: - th, July 2009

DECLARATION

I declare that the material in this dissertation entitled in Hindi ----

(((( $$$$ &&&& )))) in Hindi

Transcription HINDI-MARATHI MASHINI ANUVAAD MEIN SHAABDIK ASPASHTATA

(SAMAAN UCHHARAN VAALE SHABDON KE SANDARBHA MEIN) in English LEXICAL

AMBIGUTIY IN HINDI-MARATHI MACHINE TRANSLATION (IN THE CONTEXT OF

HOMONYMS) submitted by me is an original research work and has not been

previously submitted for any other degree in this or any other university /institution.

Kamble Prakash Abhimannu

RESEARCH SCHOLAR

PROF.CHAMAN LAL Dr. GIRISH NATH JHA PROF.CHAMAN LAL

SUPERVISOR JOINT-SUPERVISOR CHAIRPERSON

CIL / SLL&CS Special Centre for Sanskrit Studies, CIL / SLL&CS

Jawaharlal Nehru University Jawaharlal Nehru University Jawaharlal Nehru University

New Delhi 110067 New Delhi 110067 New Delhi 110067

GRAM:JAYENU TEL.:(91-001) 6107676, 6167557TELEX:031-73167 JNU IN FAX:91-011-6865886

Page | 3

VVVV

. . . . . . . . . . . . . . . . . . . . . . . . . 3-33

WXYWXYWXYWXY XXXX////////-\-\-\-\ ]]]] VVVV. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3ii-3iii

FFFF //// . . . . . . . . . . . . . . . . . . . . . . . . . . 01 -03

CCCC:::: ____ . . . . . . . . . . . . . . .. . . . . . . . . . . . . . .. . . . . . . . . . . . . . .. . . . . . . . . . . . . . . 04 22

//// CCCC . . . . . . . . . . . . . . . . . . . . . . . . . . 05-05

1. RRRR . . . . . . . . . . . . . . . . . . . . . . . . 05-06

1.1 ____ /+V/+V/+V/+V WXYWXYWXYWXY 8888 . . . . . . . . . . . . . . . . . . . . . 07-10

1.2 ____ bbbb . . . . . . . . . . . . . . . . . . . . . . . . 10-10

1.3 ........ cccc FdFdFdFd (The Source Language Analysis) ____ bbbb 11-11

2. ____ . . . . . . . . . . . . . . . . . . . . . . . . . . 11-22

2.1. & & & & (pre -Translation Problems) . . . . . . . . . . . 1212

2.2. ____ (Translation Problems) . . . . . . . . . . . . . . 1215

2.3. gggg (Post-Translation Problems) . . . . . . . . . . . 1515

2.4. ____ (Translator/User problems). . . . . . . . . . . 15-16

3. GGGG ____ . . . . . . . . . . . . . . . . . . . . . . . . . 16-17

4. ____ . . . . . . . . . . . . . . . . . . . . . . . . . . 17-22

CCCC:::: &&&& $$$$

. . . . . . . . . . . . . . .. . . . . . . . . . . . . . .. . . . . . . . . . . . . . .. . . . . . . . . . . . . . . 2361

Page | 4

CCCC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 - 24

1. &&&& RRRR RRRR . . . . . . . . . . . . . . . . . . . . . . . . . 25 - 31

2. 6 5 & $ 6 5 & $ 6 5 & $ 6 5 & $ . . . . . . . . . . . . 31- 36

3. $$$$ //// jjjjkkkk \\\\ . . . . . . . . . . . . . 36 - 40

4. llll

$ _$ _$ _$ _ . . . . . . . . . . . . . . . . . . . . . . . . . . 40 - 61

4.1 llll & $ _ & $ _ & $ _ & $ _ 40-49

4.2 l & $ _ l & $ _ l & $ _ l & $ _ 49-61

CCCC :::: ---- $$$$ ____

FFFF \\\\ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62119

C C C C . . . . . . . . . . . . . . . . . . . . . . . . 6363

1. &&&& //// &&&& FFFF . . . 64-74

2. 5F5F5F5F , , 56&56&56&56& . . . . . . . . . . . . . . . . . 74-91

3. 56565656 5F5F5F5F, , 56&56&56&56& . . . . . . . . . . . . . . . . . 91-103

4. mmmm &&&& . . . . . . . . . . . . . . . . . . . . . 104-119

CCCC : : : : &&&& $$$$ ____ ____ 8888 120-130

CCCC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121-121

1. &&&& ____ F5F5F5F5 . . . . . . . . . . . . . . . . . . . . . . . . . . 122-127

2. F-F-F-F- . . . . . . . . . . . . . . . . . . . . . . . . . . 128-130

3. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . 130-140

Page | 5

1. ____ 5555 . . . . . 130- 136

2. 5555136-140

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . .. . . .. . . . 141-148

mVkmVkmVkmVk. . . . . . . . . . . . . . . . . . . . . . . . . .. . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . 149-156

1.... mmmm . . . . . . . . . . . . . . . . . . . . . . . . 150-150

2....&&&& mmmm, , , , , , , , p\p\p\p\ . . . . . . . . . . . . . . . . . . . . . . . . . 151-155

3.... &&&& . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155-156

Page | 6

A &q FGFH ... J

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Page | 7

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Learning Form-Meaning Mappings in Presence of Homonymy: a linguistically motivated model of learning inflection by Katya Pertsova +$ |8

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WXYWXYWXYWXY XXXX //// //// -\-\-\-\

WXYWXYWXYWXY XXXX

No

Abbreviations

Description

1 ALPAC Automatic Language Processing Advisory Committee 2 COLING Computational Linguistics 3 C-DAC Centre for Development in Advance Computing 4 IIT Indian Institute of Technology 5 JSP Java Server Pages 6 NLP Natural Language Processing 7 PL Plural 8 OBJ Object 9 POS part-of-speech tagging (POS tagging or POST) 10 SG Singular 11 STT Speech to Text 12 TDIL Technology Development for Indian Language 13 TTS Text To Speech 14 VR Verb Root 15 R&D Research and Development 16 AI Artificial Intelligence 17 AUXAUXAUXAUX Auxiliary 18 PREFPREFPREFPREF Prefix 19 SGSGSGSG Singular 20 VRVRVRVR Verb Root 21 SUFFSUFFSUFFSUFF Suffix 22 SUBJSUBJSUBJSUBJ Subject 23 OPOPOPOP Output 24 FL formed language 25 LTs Language Tools 26 MTMTMTMT Machine Translation

WXYX 27 ........ 28 N 29 PRN & 30 ADJ F 31 V +V 32 NP 8 33 ADV +V-F

Page | 10

Name Page NO.

1.Table NO.1: Marathi-Hindi MT is one of the major

language pair in this project. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.Table No. 2. types of homonyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3. Table No. 3 - & $ - . . . . . . . . . . . . . . . . . . . . . . . . . . 73

4. Table No. 4. F $ - . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

5. Table No. 5 5 & 89 . . . . . . . . . . . . . 124

6. Table No. 6 j} 6A & 89 . . . . . . . . . . . . . . . . . 126

7. /8 & 5 6 & 127

-\-\-\-\

1. Graph No.1 process of MT Graph No.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2. Graph No.2 Error analysis, Frameworks example . . . . . . . . . . . . 54

3. Graph No.3 process of Homonymy word Translation Tool(Hindi to Marathi)

129

4. Graph No.4. CLIR Schematic for input being Marathi . . . . . . . . . . . . . 135

Page | 11

FFFF ////

8_ Fl$ _

F F _ C + +

/H-_(Information Technology)

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and Symbols), F5 (New Vocabulary Development)

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b + F Homonym m

Homo + onyms $ && & &

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$ & /+V & U R ,

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8 5 _ +

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$ (Homonyms) _ U

$ 8 _ FG - \$

8& G _

Page | 12

/ C _ & _

WXY 8 /8 () _

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5F , , 56& . 56 5F, ,

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F & - ~ + Homonymy $

jk $ _ + , &

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56& $ _ 5 (Language

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& C s U y /m5 5 &

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_ /m5 _ 89 Homonymy $ F5

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8 C + &

+ / 5|$ 5 - 5 -

Page | 13

- - - + /

& _ & C$ 5

_ b F +

2006 _ A

_ - / \ _ R

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Page | 14

Tabl. NO.11: Marathi-Hindi MT is one of the major language pair in this

project.

CCCC

____

FFFF ////

CCCC

1111.... RRRR

1.1 ____ /+V/+V/+V/+V WXYWXYWXYWXY 8888

1.2 ____ bbbb

1.3 ..... . . . cccc FdFdFdFd (The Source Language Analysis) ____

bbbb

2222.... ____

2.1. & & & & (pre -Translation Problems)

2.2. ____ (Translation Problems)

2.3. gggg (Post-Translation Problems)

2.4. ____ (Translator/User problems)

3. GGGG ____

4. ____

1 paradigm Shift Of Language Technology initiatives Under Programme - FG@TDIL-page 4

Page | 15

CCCC

C _ /

_ 6 , WXY 8, _ /+V

/8 (NLP) & _ _

- - b

A _ b $

F + ) G _ )

_ .. _ (Human

Translation) (Machine Translation) _

$ + . _ F

& b _

6 & _

Page | 16

1. RRRR

/ u /

U + _ y C

& + F5 y /m l 5 + +

$ , 8,

/ u $ y F| 8 +

8, _ y : _

y + p

y b &: .. 8&

& 8&g&

\ 5 u

5 \ 8& & _ F b

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y _ / $ $, jk 8$

- , m X$

8& (output) U m /Y 2

2 /. 5, , /H-_, p\, F,

/-

Page | 17

. Y .. _ R / /+V

$ (c ) (X ) y C

Machine Translation (MT) is the process of translating text units of

one language (source language) into a second language (target language) by using

computers.3

&} R + : (\) y

/8 /8

\ .. _ -

.. $ F + u

1) &: (Fully Machine Translation)

2) - (Human Aided Machine Translation)

3) - (Machine Aided Human Translation)4

1.1 ____ /+V/+V/+V/+V WXWXWXWXYYYY 8888

FG 8 F + 8

& \ 8& /

& FGFH 5 U J R& (ALPAC

Report1966) & .. & _

: .. & _ 8 8 + FG

\ U & u \

\ 8& 8& +

.. \ u \$ / u GAT,

3 CONTRIBUTIONS TO ENGLISH TO HINDI MACHINE TRANSLATION USING EXAMPLE-BASED APPROACH DEEPA GUPTA DEPARTMENT OF MATHEMATICS INDIAN INSTITUTE OF TECHNOLOGY DELHI HAUZ KHAS, NEW DELHI-110016, INDIA JANUARY, 2005 4 Punjabi Text Generation using Interlingua approach in Machine Translation (A thesis) by -SACHIN KALRA, page No.-10

Page | 18

SYSTRAN, LOGOS, METEL, TAUM-METEO, EUROTRA, ATLAS-I, ALTAS-II, TAURUS,

PIUOT, ALPA, LOGOS, CULT, TITUS, ARINC-78, METAL, MU, MT.S-NCST 5

_ / U 1983 WX 5

FGFH 5-5 TUMTS \ 5 U (In India research on

MT began in 1983 at the Tamil University in South India. The TUMTS system is a

small-scale 'direct translation' system specifically designed for Russian as SL and

Tamil as TL and running on a small microcomputer.6) + \ _ X _

\ 8& (Output) - 5

_ F- U .. U X m

\

\$ & + u ()MAT, ()ANUVADAK,

(\)MANTRA, (5)SIVA, (\)MANTR, (-6\)ANUVADAYAN- TR (CIILM)

(m-) ()ANUSARAKA ( 5), (}) Shakti (m-

, , 5) 5

\ _ A& _ Indian Language to

Indian Language Machine Translation system 85& + u F

_ /+V - 8 & _ /+V F

+ .. _ /+V l + (Human Translation) _

/+V + .. _ /+V U

& _ /+V & 5 .. _

/+V - jk

& u 6

5 F 5| / ( .) _ - . F- .

6 Recent developments in Machine Translation a review of the last five years - W.John Hutchins, (University of East Anglia, Norwich)

Page | 19

-8 5 /+V U

/+V (Text) $(LTs)

_ & /+V WXY U 8

_ /+V : - 5 / y 5 F

jA y ? C y _

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$ y _ /+V

8 & /A \

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. _ /+V _ /+V

6: _ /+V 8

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c $ - & 5

jk & \$ 5

jk & - \ 8& & 8 + \

+ j & :- 3. X j(POS TAG)

$ -6 + & 4. -6

CHANKAR MARKING 5.\ (PADSUTRA) /+V $ + +

6. /+V (WORD GRUPING) 7. F (SENSE

DISAMBIGUATION) F _ /+V A& /+V

8& _ &&

& 8& 8 /+V C

+ 8.&& 8F-(PREPOSITION MOVEMENTS) /+V

c U 5 5 $ / +

9. / (TARGET LANGUAGE GENERATION)

/

5 u

m(CORPARA )

U Fd

ANYLISAR 12.Fd(PARASAR

JR5 F

14.8&

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u + &

(Graph No.1

.. $ (Tools)

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8&

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Fd 11.E8 Fd MORPHOLOGICAL

PARASAR) 8& /5&

13.&

8 jk

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8& (Output) / _

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(Graph No.17 ref. by International Machine Translation )

(Tools) (Lexical Data) 8 8

+ c

Parallel MTS for Indian Langauagees-SRaman and N.Rama Krishna Reday,IIT,Madras IJT

Page | 20

&

10. j

FdF

MORPHOLOGICAL

/5& 5

&(GENARATER)

jk, 5,

_ 6 /+V

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by International Machine Translation )

8

/Y _

SRaman and N.Rama Krishna Reday,IIT,Madras IJT

Page | 21

/+V & & 8&

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u - - l -, ... , -

l \, ... l \, 6 l ,

+ _ | _ -/8 u, _ /

1.2 ____ bbbb

F u F

8 - /8 $ \ F5

& 5 5| _

5 _ b /8 & -

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1.3 ........ cccc FdFdFdFd (The Source Language Analysis) ____ bbbb

+ /m (\) 5 $ _ b _

/m & & /m 5

/... (NLP) $ 8

U u (A) (Recognition) (B) /+V(Understanding), (C)

(Generation) (D) /8 _ / U (/... Standard Paradigm)

(E) /} Fd (Discourse Analyser) (F) & Fd (Semantic Analyser) (G) U

Fd (Morphological Analyser) (H) c Fd (The Source Language

Analyser), (I) Fd (Syntactic Analyser) (J) / (Target

Language Generation Content Delimitation) (K) 6(Anaphora) (L)

(Syntactic Selection) (M) (Text Structuring) (N) &

(Constituent Ordering) (O) /8 (Realization) (P) (Lexical

Selection) F $ c Fd 5 +

Page | 22

F $ 6 $ _ b

8& /... + F $ , 5 ..

8& /... _ - b

2. ____

U F| /+V _ X :

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5|$, j 8$ & F (logic science) 5|$

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u

2.1. & & & & (pre -Translation Problems)

2.2. ____ (Translation Problems)

2.3. gggg (Post-Translation Problems)

2.4. ____ (Translator/User problems)

_ $ &X j

A6 u /m A6 u,

85& u 6 8 U u

2.1. &&&& :-

8 y F / J\, F ] s -

Page | 23

& u

F _ - s 8

j u 5 .. -& U

:- MT is a very expensive endeavor, both in terms of the

software development effort required and in terms of the linguistic resources which

need to be assembled.9 y _ 5 ,

8 & y u R8 g&/& y _

\$ & &

2.2 ____ :- _ / U _ /+V

: $ F u

2.2.1 8 (Language problems)

2.2.2 jk (Grammar problems)

2.2.3 F $ 8& _ (for use and development of

language tool)

2.1 8888 :- 8 (Linguistic

problems In ..) Harold Somers 8 U F +

F _ - F U F 10

2.1.1 FdFdFdFd (/8/8/8/8 5555) problems Analysis

(Apply to all NLP):- () (Lexical) () (Syntactic), ()

&(Semantic)

2.1.2. ..... j8_ j8_ j8_ j8_ . (Contrastive problems In MT)

9 Prospects for Machine Translation of the Tamil Language Fredric C. Gey University of California, Berkeley 10 Machine Translation-2- Harold Somers, CCL, UMIST. Manchester M60, IQD England, Australian Language Technology summer School, 8-9 December 2003

Page | 24

() (Lexical), () (Structural), () & (Representational),

() /}(problems in Style/Register)

() & 56 (conceptual differences), () (Lexical gaps)

1. Staructural divergence linked to lexical differences

2. Structural divergence linked to grammatical differences

3. Level shift: - Similar grammatical meanings conveyed by

Different devices

2.1.3 FdFdFdFd(Lexical Analysis)

2.1.3.1 F /+V (Segmentation)

2.1.1.1 U F _ (In Morphology):-

1. /&A UF(Functional Morphology)

2. jA UF (Derivational Morphology)

3. l4 UF(Ambiguous Morphology)

2.1.3.3 R- (Unknown words):- Misspelled, spelling, not in

Dictionary, because it a regular derivation, proper name, Compound

2.1.3.4 (Lexical Ambiguity) :-

(1) A (Structural) (A) Unstructured

(2) (F)Real ()Accidental

(3) (5)Local G(Global)

(4) FdF Analytical () G Global Ambiguity

Page | 25

(5) (Shallow) (Deep Ambiguity) : -

6 Anaphora, ()Raising, ()Case-

U R (Frame ambiguity), (R )

Quantifier and g& R (operator scope)11

2.1.3.5 (Category Ambiguities):-

1) & (Homonymy):- (C6A ) Homophones,

Homographs ( 5F ) (proper names : - many proper names form

Homonyms with meaningful words)

2). R :- (Polysemy) F l +

U {F (85&) u

2.2 jk :- 5 / jk _ U

U jk 56

/ .. j / - u

/ u FUG (kAY-1984), HPSG(Polard and Sag-1994),

LFG(Bresnsn-1982), TAG (Joshi and Schables-1992), Panini Grammar12

_ $ F jk - u

2.3 F $ 8& _ :- + .. 8&

F (Language Tools (LTs)) + \

8& 8 U u F $ _

_ A6 8& &

A $ & _

2.3.gggg :- g u +

/AX U WX (ignore)

11 Machine Translation-1- Harold Somers, CCL, UMIST. Manchester M60, IQD England, Australian Language Technology summer School, 8-9 December 2003 12 Computational Linguistics and Sanskrit Dr. Jha, Girish N., Computational Linguistics, Special Centre for Sanskrit Studies, JNU,N.Delhi-67

Page | 26

.. & /} F ; + 6 () --\

8& /} $ 8 + 8&

F 6

u, -

2.31.1 p\ (y)

2.3.1.2 5A F$ _ 8 _

2.3.1.3 y /m _

l 5 (output(OP)) 8& (Formed language) p\

5 j U / 5

F |

2.4. ____ :-

/A / + U //

g& _ u & 8& A6

\ 5 .. } 5

\ 8& & + / _

/ g& \

/ & (Translator/user)

u F & _ b _

F $ u, + _ \

A $ & & A 8

F8$ -8 Fd 5 +

& & U & &

Page | 27

R + & Fl A

\$ c \$ U u

3. GGGG ____

G A, j, -F X\ _ b

6 F8$ + A, F

l G _ _

G u FG _

\ U u 8

$ 8 j &, C8 j /& $

6 : / \ X + &

(incompleteness) F +

13 $+ y

&(incomplete) $ _ X F5

+

_ \ (quantity) + g(quality) \ \ /

c

_ 8 _ 8 X \

+ / +

& $ $ 8& 5 $ &

_ g \

/ .. _ J G

13 F 5| /( .) _ - . F-

.-

Page | 28

\$ _ u 8 $ $ _

_ 6 $ 5 G

F U G 8 / U 5 u 1)

5k +V R 2) 3) 8

/& (High function and low function term) 4) (Vagueness) 5) 6

(Anaphora) 6) (juncture) 7) _ - 5WX$ _

/U 8) -R& 9) F /& 10) $ F

(Segmentation of unit) F + +

C(mediator language) & C jAFg

(Derivational) c _ 5j} & 8UF (Represent)

+ 14 + & + &

C 8& / U G u

j F u, FG \$

U u

4. ____

_ X & U , &$

\ + +

_ & _

6 C + g& F 58 /5$

$ R$ $+ 5 R F5 +

6 F R F5 + /} -

15 .. 8& g& _ .. g& (.. users)

14 _ . &, / (y F-) /- 15 : R{b /. p\ (y F-) /-

Page | 29

u _ |8$ F +

$ } X\ }

U % m

(Sinha and Jain, 2003) b + m 5

\ 8& + Private research also in other countries but not in

India. Consequently, in a country like India, where English is understood by less than

3% of the population (Sinha and Jain, 2003), the need for developing MT systems for

translating from English into some native Indian languages is very acute.16

_

_ /+V - 8 / 5

A& + A /H-_(Language Technology)

/8 u /H-_ 8 _

5 - F5 Ab

/-/ FG _ 6 _ U

+ - F5

6 F - s

Y \$ _ c F +

- & m U

m _ 56

/ & m

\$ J _

Fl _ 5 .. _ J u +

5 \ 8& _ R8 To our knowledge

there is no direct machine translation software development being done on Indian

subcontinent languages languages spoken by nearly twenty percent of the worlds

people. The reasons are simple, the resources available for traditional machine

16 CONTRIBUTIONS TO ENGLISH TO HINDI MACHINE TRANSLATION USING EXAMPLE-BASED APPROACH DEEPA GUPTA DEPARTMENT OF MATHEMATICS INDIAN INSTITUTE OF TECHNOLOGY DELHI HAUZ KHAS, NEW DELHI-110016, INDIA JANUARY, 2005

Page | 30

translation are simply non-existent and the commercial viability of such

development seems dim.17 However, the new field of research into statistical

machine translation offers hopes that Tamil translation may be just around the

corner, to the pleasure and social benefit of all. l _

5 &: \ 8& + %

u + R \

+ F , /$ _

j8 - 5 U

_ + 5 \ U

A& Two major problems exist in connection with machine

translation and cross-language retrieval of Tamil (and other Indian languages). First

is the lack of machine-readable resources for either machine translation or cross-

language dictionary lookup. Such dictionaries need to be pared of poetic and

Classical terminology and augmented with modern words as found in recent

newspaper and Magazine texts. Tamil is a phonetic language. By default each Tamil

consonent is followed by an a vowel sound. The vowel sound is modified by the

addition of glyphs, such as the curlicue following the m which changes it sound

from ma to mi. To suppress the vowel sound one places a dot over the consonant.

This means that borrowed words from English or other languages will sound

similar in Tamil to their native language (Malten 1996).18 5 \

F + 5 ( 6 ) \ /

- 5

- 5 $ jA

F5 \$ p\ 5 5 8 R

, 8 /A 5 j F

8 l R& + + \ C8 5

R8& FF 5 j p

& m 6 5 $ 5

X\ _ -$ 17 Prospects for Machine Translation of the Tamil Language Fredric C. Gey 18 Prospects for Machine Translation of the Tamil Language Fredric C. Gey

Page | 31

_ & (-) F, $

8&, F & + _ b 5 ?F

R _ & R _ A&

-5 5- \$ +

5 5 \

+ & : & : F8$,

F8$, F8$ F8$ _ 5 /

- \ Fg F5 + -F$

- RWX HF F$ A

u, + g: 8 + y F F$

u19 -/A -, _ b $

.. _ &8 .. _ +

m \$ $ & \

- } - / , + & U

5 X + \$ 8& /8 U

\$ 8& s 8 U

1. F 2. F 3.c -

F5 4.& , 5. $ 6 _

6.$ $ _ , 7.F5 $ _

, 8. & 6 _ 9., /} _ &

_ 10. _ F $

& F5 + F l

\ 8& u

19

Page | 32

- X 5 +

\ /8 | _ /+V

5 .. _ /+V _ /+V $

8$ /8 & y /5 8 u

_ u .. l _ - 8$

8& (Output)

_ 8 + 5 , |

X\ _ $ _ -

F, $ 8&, & F &

+ _ b _ s +

, _ & &

+ + p\ F| (Artificial Intelligence) &

8U(Knowledge Representation) _ F- F5 _ u, _

/8 6 , & & /+V

y - 20 ..

$ {F 5 ,

_ J y _ }

- + 5

Fl$

5 / $ $

- F

5 U

/ - \$ 8& +

20 y F / F J\ ] s -

Page | 33

F 5

\ 5 Building translation system From

and to Tamil helps the Tamil community all over the world in accessing the

information in Tamil. 5 5 .. F &

5 FG -& & &

5 5 _ 5 5 .. _

b ..

R .(Fredric C. Gey).. $ & +

$ F5 _ Ab After having discussed

the various components of an MT system, and the resources that might be needed to

be build for MT 21

21 Prospects for Machine Translation of the Tamil Language Fredric C. Gey

Page | 34

CCCC

&&&& $$$$

CCCC

1. &&&& RRRR RRRR

2. 6 5 & $6 5 & $6 5 & $6 5 & $

3. &&&& $$$$ //// jkjkjkjk \\\\

4. llll &&&& $ _$ _$ _$ _

4.1 llll & $ _ & $ _ & $ _ & $ _

4.2 l l l l & $ _ & $ _ & $ _ & $ _

Page | 35

CCCC

l C / F & $ & F &

_ /: & + / 8& , /

& U u $

/A + / .. 8&

F _ + /F & _

8& l& .. 8&

& $ F & b $

F + & $ 6 jk \$

& _ & $ /$ l +

+ / 8& u

8 , F +

\ & U 8&

5 m- \$ & $

8& F +

\$ & $ 8 5 / +

+ & \$ _

s R{ 5 .. \$

C & _

Page | 36

1.&&&& RRRR RRRR

&q , F & 6 _

- C8 A

$ C8

C8 _ A R _

6 _ - u +

6 $()

? +

8 + 56 & 56 8 ?

8 + 6 $

56& \ u Fl$

+ 6 $

F _ +

b +

$

& } + l&

8&

5F 5 5F & _ 56

56 F-5; -; -: -;

-6 22 _ 8$ &

8& u - j} 8

5 5 , +

& j} $ ~ Fl , _

$ F & 5 C8

22

& 5 .p\ - 76

Page | 37

/ F , / F

& / J 5 -\ {b / U / +

23 y X$ -\$ U + -\$

~ y 5 & & 5 y

6 -\$ $

& , jk , s V

, 6 /H-_ 5

&

X$ -\ - -\$ 8& $

U , 6 & /Y $

$ jk /Y u $ 5 ~

$ / 5 /+V

U / + / _ U +

F / $ _ 8 Y6

U - A6 -

/F u

$

V $ F $

/ / + F5 +

F(concept), j, 8, , , + &

8& _

8 $ F R u $ R

{F u F {F F + X\ $

/ R u /: 8 $ $

23

8 F . 87

Page | 38

R- _ s F {F

$ l& 8&

u 6 $ -

Y u u + 6

$ l& (),

(g&), (), (8\), ( ), ()

_ {F $ F + -\ U

u / + u y

+ - & ? & 5

_ b _ ; F

F 5

+ $ _ (Homophony) &

(Homogaraphy ) ~ 5 A6 , /FF$

&-/ _ E 24 & $

R{ k8

Fl$ & + ..

k8 &/ m C

56& $ & _ in Sanskrit one comes across

discussion on homonymy among the suffixes. Pini (6th cent.B.C) treats them in his

Adhyyi 25 & $ & m j

&R $ &R( ) &/ F

j U F & + Among the Indian writers,

24

F 5| / (& 6? & 6 m) . 8, .

, .? , .G, .

25 Treatment of homonymy in Pini Adhyyi H.S.Ananthanarayana - page No.- 50

Page | 39

Bhartrihari(7th cent .A.D.) was probably the first to discuss this question,

systematically. 26 &R( ) & $ &

$ $ F + & $

l& + - & /F 5 &R

& U$ / syntactic

construction, / context, & goal, -A propriety, place,

time. Bhartihari lists in his vakyapadtya(2.314) six factors which may be used as

helpful cues in correctly interpreting the intended sense. They are: vakya syntactic

construction. prakarana context, artha goal, aucitya propriety, desa place and

kala time.27 / &R $ &

_ & & 8& 56&

$ 8 5 + 5

\ &: \ &

U + \ C _ +

+ A6 $

8 J $ _ +

$ l /5

/+V - .p\

9 $ 8 C 5 F _

85 (Homonymics) F5 C

& $ 8& 8

-

1. J& 6(The world book encyclopedia):- _ 8

- $ C8 6 56 & _ 5F

m & $

26

] s - 49 27

] s - 50

Page | 40

Is a language term for two or more words that have the same sound but

different meaning. The word may or may not be spelled alike Homonyms are

common in the English language.28

2. . (M.Teresa Cabrs) :- R F & $

C8 } & ( C8 )

& & ( 5F ) C8

+ 5F 56

Tradition linguistics draws a distinction within homonymy between phonological

Homonymy (Homophony) and Orthographic homonymy (homograph) homophones

are unit that are pronounced the same but are spelled differently : - e.g.night/knight,

here/hear, bare/bear, whereas homographs are words that have the same spelling

but differ in origin.29

3. . :- $ 5 u &

6 & 56 u $ 56&, C6A,

C 30 m & (8 Homonyms)

u

4. . & (M.Teresa Cabrs):- &

C8 $ U + & 56 u :- m

bank (u) p\ U 56

& :- bank + bank & u

( _ ) &

+ 5F 56 :- bare bear

& C8 $ R C8

:- mouth the mouth of the river, +

& $ & A word that sound identical to 28

The world book encyclopedia volume 9 (H) page 275-276 29

Terminology Theory, Methods and Applications - M.Teresa Cabrs, page No.111

30 , - . , ] s 28

Page | 41

another word with a different meaning; e.g. bank, pertaining to the ground

boarding a river and also pertaining to a place for keeping money. A Homonym also

be word that sound identical to another word that is spelled differently; e.g. bare

and bear Homonym is distinct from polysemy or multiple meaning; e.g. mouth as

in the mouth of the river, is a single word with two related meanings.31

&} R + ; F 6

& $ 5 6 5F 56

6 _ 5F $ _

R + & $

- + $ & 56

+ Fl & / ;

- + . & (M.Teresa

Cabrs) .. R 6$ mouth

5 C8 & $

56 5 C8 $ & $

& _ {F F + $ $ & 56 8&

8 C8 $

& $ & Fl$ & $

F ..$ & $ & m

& $ _ ..$ -

- 5

X\ \ & & $ _

8 F

& _ AFg F + m Homonym m

Homo + onyms $ && & homonyms

m Homo & onym / m

31

Encyclopedia Britannica vol.No.5.H :- page No - 107

Page | 42

Homonymy 8& 32 Homonymy 5 x8

56& /5 Homonymy 5

m- $ - R

- C 56&

5F & /Y

m Homonymy 5 &

+ , ... m-

5 & $ & + +

$ F + +

$ _ - s 6: +

FG _ - U 5

2.6 5 & $ 6 5 & $ 6 5 & $ 6 5 & $

1. () ()

(/) ()

() (&)

() ()

(A) ( )

(A) ()

g(4) g()

2. (&) ()

() ( &)

( ) ( )

(_) ( )

32

http://efl.htmlplanet.com/polysemy.htm

Page | 43

() ()

() (-J)

( _ ) (_ )

3.m be() bee ()

knot() not ()

dear(F/) deer ()

hour() our()

wind() wind()

dove (g) dove(_)

mobail () mobile(C \,)

4.&

& m & m

Morgan tomorrow () morgan morning ()

arm poor () Arm arm ()

elf eleven () Elf elf ()

Steuer tax () Steuer wheel ()

Tau dew () Tau line ()

Bis until () Biss bite ()

Boot boat () bot offered (/F)

Bund federation () bunt colorful ()

Chor chorus ( ) Korps corps (8)

das he () dass that ($+)

Heer army () her from ()

Isst eats ( ) ist is ()

Meer sea () mehr more (-)

Nahmen took (5) Namen name ()

Page | 44

Rad wheel () Rat advice ()

seid are (u) seit since ( )

Tod death (8) tot dead ()

viel much () fiel fell ()

wahr true () war was ()

Waise orphan () Weise way (&)

5.5 33

5 m 5 m

max stroke / Max stretch

cegok hoursman cegok passenger /

Beulu will pouer } Beulu freedov

Digx sprit Digx scent \

6.8 34

8 m 8 m

Solo (alone) ) slo (only) (\)

si (if) () s (yes) ()

sumo (supreme) () zumo (juice) ()

tasa (rate) () taza (cup) (y)

ti (you) () ti (note of the musical scale) ()

33

Langenslheiots, Russian Dicationary, by E.Wedel 34

www.spanicity.com

Page | 45

tu (your) () t (you) (,)

tubo (pipe) () tuvo (to have) ( )

vino (wine) () vino (to come) ()

A - F F

C & $ + m, &, 8, , 5,

5, , , , p 35 F

& $ _ {F

F + b + F

F$ _ {F & + 6: & $ F

- + /: $ _ R _ 56 +

6 j}, FH4 $ _ R U _ $

FG _ + & + / 8& u

F b FG l

F5 u b &

$ _ AFg $ _ _

& 8& - l - F5

_ 6 $, 8$ _ & F 6

$ 5 _ /+V,

/ - F5 R / , X

$ _ & $ 5

U _ -WX$

_

35

Lexical meaning page No. 75

Page | 46

+ & 8& ?

F + 8 U / u

1.5 F

2.8 b

3.RF

4.&

5.$ & /

6.$ R /

7.$ WX8Y

8.& X\ F

9.X

10.F 5

11.$ jk /

12.56& 5x, F56 -$ _ U, HA U

& 8& / 5F l8

8& 56 / _

& 8& _ /+V 8& -

& - , /: , + &

56 & 56 u

& 56 , & + $ _ s

_ m $ & $ _

/ & U C8

u & $ + $

F + , F b $ -

- - U , 6

U & $ 8 $ F

1.HOMOGRAPHS

2.HOMOPHONES

3.HETERONYMS

4.POLYSEMES

5.

CAPITONYMS36

(Pronunciation),

F u

F /$

3. $$$$

1. 5F

56

bark (the sound of a dog) and

56

front of a ship) and bow

2. C8

$ &

56

36

http://en.wikipedia.org/wiki/Homonym

Same

spelling

Different

spelling

Tabal No. 2. types of homonyms

(Pronunciation), C8 56& 8$

& $ _ (inequality)

/$ _ R 8 U

$$$$ //// jkjkjkjk \\\\

(HOMOGRAPHS) - &

56 5F $

(the sound of a dog) and bark (the skin of a tree). $

56 (heteronyms)

(a type of knot).

C8 (HOMOPHONES) C8

$ &

C8 56& , 5F

http://en.wikipedia.org/wiki/Homonym

Same

pronunciation

Different

pronunciation

Same

spelling

Different

spelling

Homonym

Homograph

Page | 47

Tabal No. 2. types of homonyms

-$

(inequality)

&

5F $

. :-

$

:- bow (the

$

$ & , &

5F

Homonym

Homograph

Homophone

Heteronym/

Heterophony

Page | 48

56& ( 56& $ _ 5F )

C8 56& :- Homographic examples include tire (to

become weary) and tire (on the wheel of a car). + 56

5F 56& (Heterographic) :- to, too, two, and there, their,

theyre.

3.- ~ (HETERONYMS) $ 5F

56& $ ~ _ $ _ 5F

+ $ 56 5 5F

+

:- desert (to abandon) and desert (arid region); row (to argue or an argument) and

row (as in to row a boat or a row of seats). $

F Heterophonys U

4. (POLYSEMES) _ 5F/&

+ & 56 - /:

56& $ $ 8 F

, : 8 +

56& u; F 8& :-

Words such as "mouth", meaning either the orifice on one's face, or the opening of a

cave or river, are polysemous and may or may not be considered homonyms.

6.5F R& & (CAPITONYMS) - 5F

+ 56 ~ s U _ &

R& 8& :- polish (to make shiny) and Polish (from Poland).

& $ F _ $

R + R

56 6 F b 56

+ 6 j}

Page | 49

5F $ 5F & 56

+ - ~ (HETERONYMS)

F: 5F - F U -

_ 5F C8 - ~ $ 5F

+ 56 5 $ 5F A

- 5F C8 5 , +

5 &

C8 $ & $ F

+ & & 8 R

5F R& & (CAPITONYMS) & $ _ \

$ 5F R&

& ; _ 5F

, + / X

, & R& & 56 A6 5F

/ 8 jk 8 U

F /$ & _ 56

/ $ - ~ (HETERONYMS) $

8& 5F

A & $ $

- A j jk

$ - A 8& & $ _

AFg jk F _ A& 5 :-

U & F 5

U & &

& 5 U Grammatical developments also sometimes lead

to the formation of homonyms. Jita as the past tense of jina, meaning alive, is also

the past tense of jitna, meaning conquered; and diya is both a lamp and the past

Page | 50

tense of dena or give.37 / $ R& $

8& &: jk & $ 8& j

8$ _ A& 5 j & $

8& $ 8 A& 5

8 :- U + 6 U

& +V U

+ & U

/ j $ U$ +

$ 8 U / _

8 U 1. A (Structural

Ambiguity) - 8& F5

8& Y6

A

2. (Lexical ambiguity):- + F5 8&

8

5 A& + y _ +

& 5 - & m

1. He chairs the session.

2. The chairs in this room are comfortable.

chairs +V U +

chairs 5 + chairs & /A

& 5 & 6 8 POS

tags C +

37

http://www.tribuneindia.com/2000/20000819/windows/roots.htm

Page | 51

3. jk jk jk jk (Grammatical ambiguity) :- /A j

8& jk 8$

A6 jk

8& & $ 8&

A

jk - /

/A

/ _

56& (The Homonymy is

a lexical ambiguity.) 38 8 Word Sense

Disambiguation (WSD) C + , 5

/A \ c j F

8$ 8& + &

8& &

/ A&

j &R 56& $ _

5 j 8$ 8& +

4. llll $ _$ _$ _$ _

l & $ _

U -, -, 5WX-

5WX, & jR-$ F$ 6

j}$ & $

38

Punjabi Text Generation using Interlingua approach in Machine Translation (A thesis) by -SACHIN KALRA, page No.- 39-40

Page | 52

6 U / 5 + + 56

/ /F ,

5 + / /$ F

+ (Interpretation) l 5k U +

- _ - j U

5k 6 6 $

y _ F

/ + A6

y _

/

$, 5$, /$ l

& - l u

F l + /

8, , & & U

& 5 J A FG _

& _ g _ A

_ U 5 l & $

8& $

8$ R- b

& $ / _

& 56

/: -- $ 5k U

+ $ F& 5 /+V 8 U

6 A &

u 8 _ /+V A6 / U

Page | 53

l& , & , u

$ ,

+V , +

+ _ _

4.1 l & $ _ l & $ _ l & $ _ l & $ _

_ /+V & $ + / 8 +

5 6 8$ 5

m A / _ 8 G

l 5k + + 6 / _ $

$ _ +

+ 6 A _

c & $

A +

8 5

6 + +

G

6 + +

/ _ 8 8

m 8 /

F

Page | 54

1. 6 , + X 39

2. 6 & & X 40

1. _$ 41

2. 8 _ .42

56 56 U +

s & + +

& /k /Y +

:-

1. l E

2. A A .

1. FG

2. -- A FG

1. , 43

2. 8 44

1. 5 5

2. A A .

1. u

2. k 8

1. FH y &

2. FH J y& .

39

- / _ 8 8 40

5 - 8 / - F 41

- / _ 8 8 42

5 - 8 / - F 43

k-/ _ 8 8 44

F| -8 / - F

Page | 55

& $ $ -

5 + $

R- 5F $ & 8

y $

l + 8 U

1. J 45

2. J -46

1. / , 47

2. / H 48

1. _ ?

2. 8 \ .

1. 5

2. .

1. +$ + +

2. / J E _ /

45

_ - / _ 8 8 46

\ - 8 / - F 47

_ - / _ 8 8 48

\ - 8 / - F

Page | 56

_ 5F 6

U / - +

, 4 5

5 :-

1. g 5

- 2. | g 5 .

1. $

2. A V F .

1.

1. .

/ A& + + 6

6$ (] s 14-15) 10 +

& U, , -, E,

, -, & , , ,

, $ 5 , , , ,

, & 5, , , , U

+ , , , , u +

+ , U + _ {F

A + /

+ / F R

/+ } -

1) 1. ! 8 + 49

2. ! 50

49

+ + - G, ] s - 12

Page | 57

2) 1. ! } R _ E 51

2. ! J R 8 b 52

3) 1.

2. , J C .

4) 1. !

2. ! g .

5) 1. + ?

2. ?

6 6 $ +

6) ! + /\ F \

7) ! |

8) ! 5$ 8$ _ 5

9) !

10) !

/ 6 5 $ 6

U +

+ \ _

/ 6 5 u

_ _ _ _

1. _

2.

1. 5

50

- + + - - , 6 / J, ] s - 16 51

+ + - G, ] s - 12 52

- + + - - , 6 / J, ] s - 16

Page | 58

2. .

/ & + 5

8 & $ &

/ 5

/ + 5 &

+ 4 b

, 5 & _

{F

G _ - 5

+ F| _ + / 5 $

- $

1. b +

2. b k

1. _ +

5

-- 53

2. J A- \ J

E _ C

.54

56 & $

_ u + $ $ _ {F

U +

53

+ + - G, ] s - 59 54

- + + - - , 6 / J, ] s - 69

Page | 59

/ + U +

+ -

5 & $ /

& $ / 8

1. _ p ! ?

2. _ , ?

+ & ,

, & -, , -, u

+ _ - _ {F /

8& + +V 5 A $+ & $

$ U$ / /

- U, , -, -, p -, -

/ $ 8& &

5 \ 8& \ 8& b

& $ 8 6 $ _

- m 5 &

\ 5 u

\$ 5 & }

+ + \$ + \

8 u? 56 ? $+

\ - $ _ /+V

5 8$ _ . (B Kumara

Page | 60

Shanmugam) /+V 56-56

$ _ /+V 6 _ 5

u b + $

$ + Machine Translation is a complex system that does

different kinds of processing over the text at various levels. As in other languages

Tamil also contains polysemous words that require a collocation dictionary to

disambiguate them. 55

8 & - {F b

4.2 l & $ _ l & $ _ l & $ _ l & $ _

8& & $ _ 5

\ & l 5

+ & 5

l + / b

8& & $ _

F

m- \ 56&

+ 5F } 8 U u

1. $ \ 8& +

\ u &2009

1.The night looked dashing in his armour

The knight looked dashing in his armour

55

Machine Translation as related to Tamil, B Kumara Shanmugam, AU-KBC Research Centre, M.I.T.,

Anna University, Chennai, India.

Page | 61

2. I have blond hair and blew eyes.

u 6 u .

I have blond hair and blue eyes.

u 6 $ $ .

3. Bambi's mother was a dear

_

Bambi's mother was a deer

_

4. He said he knew where the place was.

+ + .

He said he new where the place was.

+ .56

2. m- \ (& ) +

} 8 U u

&2009 5 \ 6

\ u :-

The night looked dashing in his armour

1.1

2.1

Shakti machine translation system

56

http://translate.google.com/translate_t#en|hi|He

Page | 62

3.} m} m} m} m- \ & \ & \ & \ & + + + +

(Homophon) } 8 U u } 8 U u } 8 U u } 8 U u

&2009 5


! r^aata ne usake kavacha men' de maaranaa dekhaa


! yoddhe ne usake kavacha men' de maaranaa dekhaa

2.I have blond hair and blew eyes.

! mein' sunaharaa kesha huuZ oora aazkhen' ud'Zaa


! mein' sunaharaa kesha oora niilaa ran'ga aazkhen' huuZ

3. Bambi's mother was a dear

! bambi kii maataa priya thaa


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Page | 67

tomorrow.

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piglets suckled .

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! paanii ne netRtva paaipon' men' se praachiina rome men' se yaatraa kiyaa


! maataa batakha ve batakha kaa bachce maar^a dikhaa sakataa hei idhara udhara

2.The baby sat in awe at the bright colors on the mobile.

! shishu mobile para chamakiile ran'gon' para vismayapuurnd-a aadara men'

beit'haa


! haalaan'ki sabse adhika pashu phira bhii chalataa phirate phira bhii hein' phira

bhii span'ja sessile

Hei

61

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Page | 68

3.All need to be present for a unanimous vote.

! sabhii zaruurata honaa ekamata mata kaa honaa


! mein' zaruurata honaa merii bahana vaha janmadina kaa khZariidanaa

4.The sow suckled her newborn piglets.

! sov ne ve navajaata ghen't'e stana se duudha pilaaye


! kisaana oat boegaa mein' piiche kaa forty

5. speaker please wind up your speech.

! vaktaa aapakii bolii para havaa khusha karataa hei


! havaa ud'Zii se uttara puurva62

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