A field in computer science …that’s concerned with the interaction between computer and human(natural) languages.

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2. Artificial Intelligence:
“ NATURAL LANGUAGE
PROCESSING ”
(NLP)
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3. What is NLP?
Natural Language Processing(NLP):
- a field of computer science …that’s concerned with the
interaction between computer and human(natural)
languages.
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4. Definition
• It is defined as a software and hardware components in computer
system which analyze or synthesize spoken or written language.
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5. History
NATURAL language processing (NLP) began in 1950 when Alan
Turing published his paper entitled “Computing Machinery and
Intelligence,” from which the so-called Turing Test emerged.
Turing basically asserted(belief confidently) that a computer could be
considered intelligent if it could carry on a conversation with a human
being without the human realizing they were talking to a machine.
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6. Goal
The goal of natural language processing is to allow that kind
interaction so that non-programmers can obtain useful information
from computing systems.
To build intelligent sytems that can iteract with human beings as like
beings.
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7. Example
The HAL 9000 computer in Stanley Kubrick’s film 2001: A Space
Odyssey
• HAL is an artificial agent capable of such advanced language processing
behavior as speaking and understanding English, and at a crucial moment in
the plot, even reading lips.
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HAL

8. HAL 9000 is a fictional character in Arthur C. Clarke's Space Odyssey series. First
appearing in 2001: A Space Odyssey, HAL
HAL (Heuristically programmed ALgorithmic computer) is
a sentient computer (or artificial general intelligence) that controls the systems of
the Discovery One spacecraft and interacts with the ship's astronaut crew.
The language-related parts of HAL
• Speech recognition
• Natural language understanding (and, of course, lip-reading),
• Natural language generation
• Speech synthesis
• Information retrieval
• information extraction and
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9. Solving the language-related problems and others like them, is the
main concern of the fields known as Natural Language Processing,
Computational Linguistics, and Speech Recognition and Synthesis,
which together we call Speech and Language Processing(SLP).
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10. • Dave: Open the pod bay doors, HAL.
• HAL: I am sorry, Dave. I am afraid I can’t do that.
• Dave: What’s the problem.
• HAL: I think you know what the problem is just as well as I do.
• Dave: I don’t know what you’re talking about.
• HAL: I know that you and Frank were planning to disconnect me, and I’m afraid
that’s something I cannot allow to happen.
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General speech and language understanding and generation capabilities
Politeness: emotional intelligence
Self-awareness: a model of self, including goals and plans
Belief ascription: modeling others; reasoning about their
goals and plans

11. • Hal: I can tell from the tone of your voice, Dave, that you’re upset.
• Why don’t you take a stress pill and get some rest.
• [Dave has just drawn another sketch of Dr. Hunter].
• HAL: Can you hold it a bit closer?
• [Dave does so].
• HAL: That’s Dr. Hunter, isn’t it?
• Dave: Yes.
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Recognition of emotion from speech
Vision capability including visual recognition of emotions and faces
Also: situational ambiguit

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13. Application of
Natural LanguagE Processing
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14. Speech processing: get fight information or book a hotel over the phone
Information extraction: discover names of people and events they participate in , from a
document
Machine translation: translation a document from one human language into another
Question answering: find answers to natural language questions in a text collection or
database
Summarization: generate a short biography of Naon Chomsky from one or more news
articles
Parsing : indentifing sentence structure = S->NP+ VP
Automatic speech recognition(ASR): auto transcription of spoken content to electronic
text
Speech to speech: translating spoken content from one language to another in real time
or offline .
Spelling and Grammer Corrections
Voice recogination
Text processing
POS tagging
Text to speech
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15. Information Extraction
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16. Question Answering
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17. Part of Speech (POS) recognition
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18. Parsing
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19. Why NLP needed?
Huge amount of data (from 2013 data )
• 759 Million - Total number of websites on the Web
• 510 Million - Total number of Live websites (active).
• 103 Million - Websites added during the year i.e 2013
• 43% of the top 1 million websites are hosted in USA itself.
• 48% of the top 100 blogs/websites run on powerful WordPress.
• 23% - Increase in the average page size of a website.
• 13% - Decrease in the average page-load time.
# application for processing large amounts of texts and other data
#on one of the application of Natural Language Processing
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20. “In the,” writes Marc Maxson, “the most useful data will be the
kind that was is too unstructured to be used in the past.” [“The
future of big data is quasi-unstructured,”Chewy Chunks, 23
March 2013] Maxson believes, “The future of Big Data is neither
structured nor unstructured. Big Data will be structured by
intuitive methods (i.e., ‘genetic algorithms’), or using inherent
patterns that emerge from the data itself and not from rules
imposed on data sets by humans.”
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21. System that can sense, think, learn, and act is going to be up to the
challenge of performing natural language processing. Our Cognitive
(understanding through) Reasoning Platform uses a combination of
artificial intelligence and the world’s largest common sense ontology
(the branch of metaphysics dealing with the nature of being) to help
identify relationships and put unstructured data in the proper context.
The reason that a learning system is necessary is because the veracity
(accuracy) of data is not always what one would desire.
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22. Most analysts appear to agree that the next big thing in IT is going to
involve semantic search. It’s going to be a big thing because it will allow
non-subject matter experts to obtain answers to their questions using
only natural language to pose their queries. The magic will be
contained in the analysis that goes into the search that leads to
answers that are both relevant and insightful.
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24. Steps in NLP
Phonetics , Phonology
Morphological analysis
Syntactic analysis
Sementic analysis
Pragmatic analysis
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25. Photetic Analysis
Construct words from phonemes through frequency spectrogram
Eg. Th-i-ng = thing
Phoneme Database is used.
(distinct units of sound in a specified language that distinguish one
word from another, for example p, b, d, and t in the English
words pat, bad, and bat.)
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26. Morphological Analysis
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27. Syntactic Analysis
1. Abstract result of Phonetic analysis
2. Build structural description sentence.
3. Flat input sequence is converted into hierarchical structure (parsing).
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28. Semantic analysis
The study of meaning. It focuses on the relationship between
signifiers—like words, phrases, signs, and symbols—and what they
stand for, their denotation.
Generates partial meaning /representation from its syntactic structure
Eg. “plant”= industrial plant
“plant”=living organism
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29. Pragmatic Analysis
Uses context of utterance
where , by who , to whom , why , when it was said
Eg. “ RAM eats apple .He likes them.”
he=“Ram”
them=“apples”
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31. NLP in other domain
Bio-medical
Forensic Science
Advertisement
Education
Politics
Bussiness development
Marketing
And where ever we use language !!!
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32. Future of NLP
 Sematic web/ search
 Sentiment analysis /opinion mining
 Machine translation
 Advance speech processing application
 Social network analysis
 Collective intelligence
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33. Conclusion
• Software programs are applied to a wide range of analysis fields,such
as named-entity extraction , deep analytics , opinion mining ,
sentence segmentation.
• Ideally , NLP will influence the developemnet of programming
languages, and computer programming will use natural human
languages rather than specialized codes for development.
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