Natural Language Processing-(NLP).pptx
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- 3. INTRODUCTION • What is Natural Language Processing • NLP for machines • Why NLP • History of NLP 3
- 4. What is natural language processing? • Process information contained in natural language text • Also known as Computational Linguistics (CL), Human Language Technology (HLT), Natural Language Engineering (NLE) 4
- 5. NLP for machines… • Analyze, understand and generate human languages just like humans do • Applying computational techniques to language domain • To explain linguistic theories, to use the theories to build systems that can be of social use • Started off as a branch of Artificial Intelligence • Borrows from Linguistics, Psycholinguistics, Cognitive Science & Statistics • Make computers learn our language rather than we learn theories 5
- 6. Why NLP? • A hallmark of human intelligence • Text is the largest repository of human knowledge and is growing quickly • computer programmes that understood text or speech 6
- 7. History of NLP • In 1950, Alan Turing published an article titled "Machine and Intelligence" which advertised what is now called the Turing test as a subfield of intelligence • Some beneficial and successful Natural language systems were developed in the 1960s were SHRDLU, a natural language system working in restricted "blocks of words" with restricted vocabularies was written between 1964 to 1966 7
- 8. COMPONENTS AND PROCESS • Components of NLP • Linguistics and Language • Steps of NLP • Techniques and Methods 8
- 9. Components of NLP • Natural Language Understanding (NLU) • Taking some spoken/typed sentence and working out what it means • Natural Language Generation(NLG) • Taking some formal representation of what you want to say and working out a way to express it in a natural (human) language (e.g., English) 9
- 10. Components of NLP (cont.) • Natural Language Understanding(NLU) • Mapping the given input in the natural language into a useful representation • Different level of analysis required: • morphological analysis • syntactic analysis • semantic analysis • discourse analysis 10
- 11. Components of NLP (cont.) • Natural Language Generation(NLG) • Producing output in the natural language from some internal representation • Different level of synthesis required: • deep planning (what to say) • syntactic generation • NL Understanding is much harder than NL Generation. But, still both of them are hard 11
- 12. Linguistics and language • Linguistics is the science of language • Its study includes: • Sounds which refers to phonology • Word formation refers to morphology • Sentence structure refers to syntax • Meaning refers to semantics • Understanding refers to pragmatics 12
- 13. Steps of NLP Morphological and Lexical Analysis Syntactic Analysis Semantic Analysis Discourse Integration Pragmatic Analysis 13
- 14. Morphological and Lexical Analysis • The lexicon of a language is its vocabulary that includes its words and expressions • Morphology depicts analyzing, identifying and description of structure of words • Lexical analysis involves dividing a text into paragraphs, words and the sentences 14
- 15. Syntactic Analysis • Syntax concerns the proper ordering of words and its affect on meaning • This involves analysis of the words in a sentence to depict the grammatical structure of the sentence • The words are transformed into structure that shows how the words are related to each other • Eg. “the girl the go to the school”. This would definitely be rejected by the English syntactic analyzer 15
- 16. Semantic Analysis • Semantics concerns the (literal) meaning of words, phrases, and sentences • This abstracts the dictionary meaning or the exact meaning from context • The structures which are created by the syntactic analyzer are assigned meaning • E.g.. “colorless blue idea” .This would be rejected by the analyzer as colorless blue do not make any sense together 16
- 17. Discourse Integration • Sense of the context • The meaning of any single sentence depends upon the sentences that precedes it and also invokes the meaning of the sentences that follow it • E.g. the word “it” in the sentence “she wanted it” depends upon the prior discourse context 17
- 18. Pragmatic Analysis • Pragmatics concerns the overall communicative and social context and its effect on interpretation • It means abstracting or deriving the purposeful use of the language in situations • Importantly those aspects of language which require world knowledge • The main focus is on what was said is reinterpreted on what it actually means • E.g. “close the window?” should have been interpreted as a request rather than an order 18
- 19. Natural Language Generation(NLG) • NLG is the process of constructing natural language outputs from non-linguistic inputs • NLG can be viewed as the reverse process of NL understanding • A NLG system may have three main parts: • Discourse Planner what will be generated. which sentences • Surface Realizer realizes a sentence from its internal representation • Lexical Selection selecting the correct words describing the concepts 19
- 20. Techniques and methods • Machine learning • The learning procedures used during machine learning • Automatically focuses on the most common cases • Whereas when we write rules by hand it is often not correct at all • Concerned on human errors 20
- 21. Techniques and methods • Statistical inference • Automatic learning procedures can make use of statistical inference algorithms • Used to produce models that are robust (means strength) to unfamiliar input e.g. containing words or structures that have not been seen before • Making intelligent guesses 21
- 22. Techniques and methods • Input database and Training data • Systems based on automatically learning the rules can be made more accurate simply by supplying more input data or source to it • However, systems based on hand- written rules can only be made more accurate by increasing the complexity of the rules, which is a much more difficult task 22
- 23. CONCLUSION • NLP vs. Computer Language • Future of NLP • Summery 23
- 24. Natural language vs. Computer language • Ambiguity is the primary difference between natural and computer languages • Formal programming languages are designed to be unambiguous • They can be defined by a grammar that produces a unique parse for each sentence in the language • Programming languages are also designed for efficient (deterministic) parsing • They are deterministic context-free languages (DCFLs) 24
- 25. Future of NLP • Human level or human readable natural language processing is an AI-complete problem • It is equivalent to solving the central artificial intelligence problem and making computers as intelligent as people • Make computers as they can solve problems like humans and think like humans as well as perform activities that humans cant perform and making it more efficient than humans 25
- 26. Cont.… • NLP's future is closely linked to the growth of Artificial intelligence • As natural language understanding or readability improves, computers or machines or devices will be able to learn from the information online and apply what they learned in the real world • Combined with natural language generation, computers will become more and more capable of receiving and giving useful and resourceful information or data 26
- 27. Summery • The need for disambiguation makes language understanding difficult • Levels of linguistic processing: • Syntax , Semantics, Pragmatics • Statistical learning methods can be used to: • Automatically learn grammar • Compute the most likely interpretation based on a learned statistical model • Make intelligent guesses 27