This document discusses natural language processing and machine translation. It describes natural language processing as a field concerned with interactions between computers and human languages. It then lists some common NLP applications like question answering, text categorization, and machine translation. The document discusses rule-based and statistical approaches to machine translation and some issues in machine translation like word order differences between languages and ambiguity. It also briefly describes some existing machine translation systems like Systran, Bing Translator, and Google Translator.

1. Presented By:
Rajendra Verma
M.E(CSE)2ndYear

2. 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

3. Question answering.
Text Categorization/Routing.
Text Mining.
Machine Translation.
Spelling Correction.
NLP APPLICATIONS

6. 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.

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

8. RULE BASED MACHINE TRANSLATION
(RBMT)
STATISTICAL MACHINE TRANSLATION
(SMT)

9. • 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 APPROACH
GRAMMAR
RULE
LEXICON
SOFTWARE
PROGRAM

10. ANALYSIS
TRANSFER
GENERATION
सीता बाग में सोयि
ANALYSIS

11. 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.

12. 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.

13. • 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.

14. 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

15. YOUTHANK