ENGLISH TO HINDI STATISTICAL MACHINE TRANSLATION

SYSTEM

Presented By: Rajendra Verma

M.E(CSE)2ndYear

Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages.

Natural Language Processing

Question answering.Text Categorization/Routing.Text Mining.Machine Translation.Spelling Correction.

NLP APPLICATIONS

WHAT IS MACHINE TRANSLATIONA piece of text which has been written

Again automatically from one language to anotherBy a machine.

NEED OF MACHINE TRANSLATION ?

72.4% 56.2%

RESEARCH BY COMMON SENSE ADVISORY FIRM

72.1%

72.1 percent of the consumers spend most or

all of their time on sites in their own

language

72.4 percent say they would be more likely to

buy a product with information in their own

language

56.2 percent say that the ability to obtain

information in their own language is more

important than price.

Real-time communications where it would not be practical for a human to translate (e.g. chat and email.)

AREA’S IN WHICH MACHINE TRANSLATION IS USEFUL

TYPES OF MACHINE TRANSLATION

MACHINE TRANSLATION

RULE BASED MACHINE TRANSLATION (RBMT)

STATISTICAL MACHINE TRANSLATION (SMT)

• Rules-based systems use a combination of language and grammar rules plus dictionaries for common words. Specialist dictionaries are created to focus on certain industries or disciplines.

RULE BASED MACHINE TRANSLATION

RULE BASED APPROACH

GRAMMAR RULE

LEXICON SOFTWARE PROGRAM

Sita Slept in Garden

ANALYSIS

Gardenin Slept Sita

SVO

TRANSFER

Garden in Slept Sita

SOV GENERATION

सीता बाग में सोयि�

Sita Slept in Garden

ANALYSIS

Gardenin Slept Sita

STATISTICAL MACHINE TRANSLATION• Statistical machine translation (SMT) learns how

to translate by analyzing existing human translations (known as bilingual text corpora).

• Machine translator can use a database as the source for all the information it need for translating.

ISSUES IN MACHINE TRANSLATION• Word order

Word order in languages differs. Some classification can be done by naming the typical order of subject (S), verb (V) and object (O) in a sentence . Some languages have word orders as SOV. The target language may have a different word order. In such cases, word to word translation is difficult. For example, English language has SVO and Hindi language has SOV sentence structure.

• Ambiguity A given word or sentence can have more than one meaning.For ex, the word ‘’party’’ could mean a polytical party, or a social event,and deciding the suitable one in perticular case is crucial to getting right analysis and therefore right translation • The third reason is that when human use natural

language, they use an enormous amount of common sense, and knowledge about the world, which helps to resolve the ambiguity. For ex. in ‘’He went to the bank, but it was closed for lunch’’,we can infer that ‘bank’ refers to a financial institution, and not a river bank, because we know from our knowledge of the world that only the former type of bank can be closed for lunch.

SYSTRAN TRANSLATOR• RULE BASED MACHINE TRANSLATION

SYSTEM.• SUPPORT 45 LANGUGAES.

BING TRANSLATOR• STATISTICAL BASED MACHINE TRANSLATION.• SUPPORT 47 LANGUGAES.

GOOGLE TRANSLATOR• STATISTICAL BASED MACHINE TRANSLATION• SUPPORT 80 LANGUAGES.

EXISTING MACHINE TRANSLATION

YOUTHANK