Natural language processing
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The document provides an overview of natural language processing (NLP), detailing its definition, history, components, and methods. It emphasizes the importance of NLP in enabling machines to understand and generate human language and discusses the challenges posed by ambiguity in natural languages compared to formal programming languages. The conclusion addresses the future of NLP, relating it to advancements in artificial intelligence and its potential to enhance human-like problem-solving abilities in machines.
Natural language processing
- 1. CMIS 4114 Index : 122139 Natural Language Processing
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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 theirs 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 • Taking some spoken/typed sentence and working out what it means • Natural Language Generation • 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 • 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 • 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 13 Morphological and Lexical Analysis Syntactic Analysis Semantic Analysis Discourse Integration Pragmatic Analysis
- 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 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 two 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 (DCLFs) 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